Reviewerly/exHarmony huggingface dataset:
A bootstrap approach for validating the number of groups identified by latent class growth models

The use of longitudinal finite mixture models such as group-based trajectory modeling has seen a sharp increase during the last decades in the medical literature. However, these methods have been criticized especially because of the data-driven modelling process which involves statistical decision-making. In this paper, we propose an approach that uses bootstrap to sample observations with replacement from the original data to validate the number of groups identified and to quantify the uncertainty in the number of groups. The method allows investigating the statistical validity and the uncertainty of the groups identified in the original data by checking if the same solution is also found across the bootstrap samples. In a simulation study, we examined whether the bootstrap-estimated variability in the number of groups reflected the replication-wise variability. We also compared the replication-wise variability to the Bayesian posterior probability. We evaluated the ability of three commonly used adequacy criteria (average posterior probability, odds of correct classification and relative entropy) to identify uncertainty in the number of groups. Finally, we illustrated the proposed approach using data from the Quebec Integrated Chronic Disease Surveillance System to identify longitudinal medication patterns between 2015 and 2018 in older adults with diabetes.
Variational Auto-Encoder with Student's t Prior: Excerpt

This text is an excerpt of the paper "Variational Auto-Encoders with Student‘s t Prior" (authors: N. Abiri and M. Ohlsson) presented at the European Symposium on Artificial Neural Networks and Machine Learning 2019. We provide some extra details to help in implementing the approach suggested in the paper and to further understand the technical part.
Merintis Literasi Masyarakat Melalui One Home One Library di Kelurahan Sukoharjo Jawa Tengah

This community service aims to realize the RW Literacy Village. 01 Sukoharjo Village through the One Home One Library Community Literacy Movement. Partners The target of this service is the RW. 01 Kelurahan Sukoharjo consists of 4 neighborhood units (RT), starting from RT. 01 to RT. 04. The method of implementing community service activities is through socialization of the establishment of literacy villages, reading week activities with children and youth in the neighborhood, internet surfing week, and digital literacy trainings. The result of this service activity is to provide social change in the RW. 01 Kelurahan Sukoharjo is good in the fields of education, lifestyle, and improving the literacy culture of the community.
Blockchain-based Secure Healthcare for Cardio Disease Prediction

Abstract: Heart disease is the commonest explanation for death globally. According to a recent study by the Indian Council of Medical Research (ICMR), about 25% of deaths between the ages of 25-69 years cause due to of different heart-related problems. Cardiovascular diseases are the highest increased diseases. The shortage of specialists and high wrongly diagnosed cases have necessitated the necessity to develop a quick and efficient detection system. So weshould always even have jumped on techniques and methods used for alertness and care to avoid the overtime of the people due to the guts attack. By applying machine learningtechniques the prediction of the disease is often done. Blockchain technology has the potential to avoid fraud and data leakage. It can make better coordination between patient and hospital. The proposed system increases data security and removes the cost, time, and resources required to manage the patient's data and results. Keywords Blockchain Healthcare Machine LearningArrhythmia
Burrows-Wheeler Post-Transformation with Effective Clustering and Integer Coding Through Vectorization

Abstract This research is mainly based on lossless integer Compression. In this research one of the most renowned method called Burrows-Wheeler transform (BWT) has been used. This method is used to sort data reversibly. After this transformation, most of the repeated characters come together. The result of BWT has been passed through Run Length Encoder (RLE) module to get the Run characters, Run Length and Run Character frequencies. The Run characters and Run Character Frequencies have been passed through Move-to-Front (MTF) module to get MTF number and final MTF list. Then the MTF number and Run length, which are non-negative integers, have been sorted using counting sort module. This module sorts the numbers according to the Run characters. The result is non-negative integers which have been coded using interpolative coding method. This method is also known as Non-statistical and Non-parametric Coding. The data has been decompressed using the same modules as compression but in reverse order. It results lossless output. To implement vectorization big files are broken into chunks and passed through each module, creating new processes. So, it works in parallel. This method results speed boost exponentially with file size while keeping compression factor intact if not better.
Mathematics and Poetry—Epilogue for a Special Issue

The Special Issue “Mathematics and Poetry, with a View towards Machine Learning”, started with three guiding questions from the guest editor:What are the similarities (and differences) between mathematical problems and poetic works [...]
Design of a Professional Sports Competition Adjudication System Based on Data Analysis and Action Recognition Algorithm

This paper uses data analysis and action recognition algorithms to conduct in-depth research and analysis of professional sports competition judging and designs a professional sports competition-assisted judging system for use in actual judging. In this paper, a wearable motion capture system based on an inertial sensing unit is developed and designed for kayaking technical motion monitoring to achieve the acquisition, analysis, and quantitative evaluation of kayaker motion data. To limit the gyroscope and pose estimation error, a gradient descent method is used for multisensor data fusion to achieve athlete pose update, and a quaternion-driven human skeletal vector model is proposed to reconstruct the kayaker’s paddling technical movements. By calculating the angular sequences of the left shoulder, right shoulder, left elbow, and right elbow joints of the athlete’s upper limbs and comparing them with the optical motion capture system, the results show that the motion capture system developed in this paper is comparable to the optical motion capture system in terms of measurement accuracy. It ultimately affects the result of pose estimation. Therefore, high-resolution networks and low-resolution networks can continuously maintain high-resolution features of the image by allowing each representation layer to repeatedly accept the representation information of other networks. A step matrix model is constructed to encode the multiscale global temporal information of action sequences, and action classification is achieved by calculating the response of the step matrix of test samples to the step matrix of each category of actions. The algorithm achieves 78.96%, 91.84%, and 91.18% accuracy of action classification on the Northwestern-UCLA database, MSRC-12 database, and CAD-60 database, respectively. The designed visual motion tracking system was applied to record the motion data of the experimental subjects in the fine motion assessment task and construct the motion assessment database. The experimental results show that the average error between the prediction results of the proposed action assessment method and the manual scoring is 1.83, and the automated assessment of fine movements is effectively realized.
Ensemble Clustering via Co-association Matrix Self-enhancement

Ensemble clustering integrates a set of base clustering results to generate a stronger one. Existing methods usually rely on a co-association (CA) matrix that measures how many times two samples are grouped into the same cluster according to the base clusterings to achieve ensemble clustering. However, when the constructed CA matrix is of low quality, the performance will degrade. In this paper, we propose a simple yet effective CA matrix self-enhancement framework that can improve the CA matrix to achieve better clustering performance. Specifically, we first extract the high-confidence (HC) information from the base clusterings to form a sparse HC matrix. By propagating the highly-reliable information of the HC matrix to the CA matrix and complementing the HC matrix according to the CA matrix simultaneously, the proposed method generates an enhanced CA matrix for better clustering. Technically, the proposed model is formulated as a symmetric constrained convex optimization problem, which is efficiently solved by an alternating iterative algorithm with convergence and global optimum theoretically guaranteed. Extensive experimental comparisons with twelve state-of-the-art methods on eight benchmark datasets substantiate the effectiveness, flexibility and efficiency of the proposed model in ensemble clustering. The codes and datasets can be downloaded at https://github.com/Siritao/EC-CMS.
Selling Safely: Cybersecurity Best Practices for Small, Rural Ag Businesses

Cyberattacks are a continued and growing threat for all businesses. Small businesses, like agricultural and rural businesses, should not think they are immune to this threat: obscurity is not security. This new 6-page publication of the UF/IFAS Department of Agricultural Education and Communication includes some cybersecurity best practices and links to resources for further education. Written by Lauri M. Baker, Cheryl R. Boyer, and Russell Boyer.https://edis.ifas.ufl.edu/wc416
Research on the Application of Virtual Reality (VR) in Museum Intelligent System

VR technology is characterized by interactivity, immersion and strong imagination, so it has been widely used in museum exhibition design. Based on the disadvantages of the museum's previous exhibition design, this paper analyzes the application prospect of VR technology in the future, and makes a lot of discussions on the specific application of VR technology in the museum's exhibition design, hoping to provide effective suggestions for the museum's specific exhibition design.
the role of the environment in indonesia languange learning

Language is a means of communication acquired by humans from birth. Mastery of a language by a child begins with acquiring the first language which is often called the mother tongue. Language is essentially an arbitrary sound symbol system used by members of social groups to work together, communicate, and to define themselves. The environment is an aspect that involves many things, including comprehensive learning. The environment can play a role especially in learning Indonesian. It affects the quality of student learning.
Lattices, Homomorphic Encryption, and CKKS

This is a survey on some topics in Lattice based cryptography and Homomorphic Encryption. In particular, we define some lattice problems, LWE and RLWE, and state the reductions given by Regev and Peikert. We also give a full treatment of the recent CKKS homomorphic encryption scheme and give some worked out examples.
Augmented Reality Appendages for Robots: Design Considerations and Recommendations for Maximizing Social and Functional Perception

In order to address the limitations of gestural capabilities in physical robots, researchers in Virtual, Augmented, Mixed Reality Human-Robot Interaction (VAM-HRI) have been using augmented-reality visualizations that increase robot expressivity and improve user perception (e.g., social presence). While a multitude of virtual robot deictic gestures (e.g., pointing to an object) have been implemented to improve interactions within VAM-HRI, such systems are often reported to have tradeoffs between functional and social user perceptions of robots, creating a need for a unified approach that considers both attributes. We performed a literature analysis that selected factors that were noted to significantly influence either user perception or task efficiency and propose a set of design considerations and recommendations that address those factors by combining anthropomorphic and non-anthropomorphic virtual gestures based on the motivation of the interaction, visibility of the target and robot, salience of the target, and distance between the target and robot. The proposed recommendations provide the VAM-HRI community with starting points for selecting appropriate gesture types for a multitude of interaction contexts.
Multi-Label Logo Recognition and Retrieval based on Weighted Fusion of Neural Features

Classifying logo images is a challenging task as they contain elements such as text or shapes that can represent anything from known objects to abstract shapes. While the current state of the art for logo classification addresses the problem as a multi-class task focusing on a single characteristic, logos can have several simultaneous labels, such as different colors. This work proposes a method that allows visually similar logos to be classified and searched from a set of data according to their shape, color, commercial sector, semantics, general characteristics, or a combination of features selected by the user. Unlike previous approaches, the proposal employs a series of multi-label deep neural networks specialized in specific attributes and combines the obtained features to perform the similarity search. To delve into the classification system, different existing logo topologies are compared and some of their problems are analyzed, such as the incomplete labeling that trademark registration databases usually contain. The proposal is evaluated considering 76,000 logos (7 times more than previous approaches) from the European Union Trademarks dataset, which is organized hierarchically using the Vienna ontology. Overall, experimentation attains reliable quantitative and qualitative results, reducing the normalized average rank error of the state-of-the-art from 0.040 to 0.018 for the Trademark Image Retrieval task. Finally, given that the semantics of logos can often be subjective, graphic design students and professionals were surveyed. Results show that the proposed methodology provides better labeling than a human expert operator, improving the label ranking average precision from 0.53 to 0.68.
Steered discrete-time quantum walks for engineering of quantum states

We analyze the strengths and limitations of steered discrete time quantum walks in generating quantum states of bipartite quantum systems comprising of a qubit coupled to a qudit system. We demonstrate that not all quantum states in the composite space are accessible through quantum walks, even under the most generalized definition of a quantum step, leading to a bifurcation of the composite Hilbert space into "walk-accessible states" and the "walk-inaccessible states". We give an algorithm for generating any walk-accessible state from a simple-to-realize product state, in a minimal number of walk steps, all of unit step size. We further give a prescription towards constructing minimal quantum walks between any pair of such walk-accessible states. Linear optics has been a popular physical system for implementing coin-based quantum walks, where the composite space is built up of spin and orbital angular momenta of light beams. We establish that in such an implementation, all normalized quantum states are "walk-accessible". Furthermore, any generalized quantum step can be implemented upto a global phase using a single q-plate and a pair of homogeneous waveplates. We then give a quantum walk based scheme for realizing arbitrary vector beams, using only q-plates and waveplates.
About the new image of the universe in the postnonclassical era: the mythological aspect

Processes unknown to science that precede the appearance of the physical Universe were analyzed. A new mythology of the universe has been developed, the symbol of which is the forever bursting butterfly. The paradigm of the transformation of the universe is revealed. The similarity of the positions of ancient mythologists and the synergistic picture of the world has been revealed. It has been shown that the energy of yang-yin and five primary elements is the mechanism of changes in the universe. The rhythm of Genesis is classified, the role of humanity in the functioning of the universe is analyzed. The energy model of being is presented. It is proved that the space of the Universe has a crystalline structure.
SELF-ASSESSMENT OF LATVIAN SUPERVISORS’ DIGITAL COMPETENCE

The importance of digital competence (Dc) is significantly increasing as global digital transformation continue rapidly to develop and digital solutions are extensively integrating into all areas. As a result, Dc is included as one of eight core competences for lifelong learning. The present study aimed to find out the self-assessment of the importance (an assessment of how significant and necessary a certain value is) and attainability (an assessment of how attainable a certain value is) of Latvian supervisors’ Dc. Digital competence self-assessment questionary is theoretically based on a major European Union research project, DigComp 2.1 and identifies the key components of Dc in 5 areas, namely, information and data literacy, communication and collaboration, digital content creation, safety and problem solving. 56 respondents participated in the online survey during October 2021. The importance and attainability indicators of Dc statements were evaluated on a 5-point Likert scale. The study indicated rather high importance of Dc (3 of 51 statements were evaluated as very important, 43 as rather important and 5 as moderately important). The indicators of the attainability of Dc varied from rather unattainable to rather attainable (26 of 51 statements were evaluated as rather attainable, 21 as average attainable and 4 as rather unattainable). Wilcoxon signed-rank test indicated statistically significant differences in 34 out of 51 statements of Dc that was evaluated higher in importance than attainability. The study results show high level of supervisors’ awareness of the digital competence importance. The obtained attainability results allowed to highlight the need for the further digital competence improvement for the supervisors, especially at such digital competence areas as safety and problem solving.
DEVELOPMENT OF STUDENTS 'CREATIVE ABILITIES THROUGH THE IMITATION-GAME APPROACH TO TRAINING ORGANIZATIONS

The ways of development creative faculty of the students are examined in this article. The examples of the business-like l&gt;lays that were suspended in the articles, are used in process of і н&gt;. training psycho-pedagogic discipline.
Prioritizing Corners in OoD Detectors via Symbolic String Manipulation

For safety assurance of deep neural networks (DNNs), out-of-distribution (OoD) monitoring techniques are essential as they filter spurious input that is distant from the training dataset. This paper studies the problem of systematically testing OoD monitors to avoid cases where an input data point is tested as in-distribution by the monitor, but the DNN produces spurious output predictions. We consider the definition of "in-distribution" characterized in the feature space by a union of hyperrectangles learned from the training dataset. Thus the testing is reduced to finding corners in hyperrectangles distant from the available training data in the feature space. Concretely, we encode the abstract location of every data point as a finite-length binary string, and the union of all binary strings is stored compactly using binary decision diagrams (BDDs). We demonstrate how to use BDDs to symbolically extract corners distant from all data points within the training set. Apart from test case generation, we explain how to use the proposed corners to fine-tune the DNN to ensure that it does not predict overly confidently. The result is evaluated over examples such as number and traffic sign recognition.
INTERPRETABLE PREDICTIVE MODEL OF NETWORK INTRUSION USING SEVERAL MACHINE LEARNING ALGORITHMS

Network intrusion is any unauthorized activity on a computer network. Attacks on the network computer system can be devastating and affect networks and company establishments. Therefore, it is necessary to curb these attacks. Network Intrusion Detection System (NIDS) contributes to recognizing the attacks or intrusions. This paper explains the factors that influence network attacks. Some machine learning methods are used such as are logistic regression, random forest XGBoost, and CatBoost. The best model is chosen from these models based on its accuracy level. Classification modeling is divided into two types, namely using a dummy and not using dummy variables. The best method for predicting network intrusion is a random forest with a dummy variable that has an Area Under Curve (AUC) value of 92.31% and an accuracy of 90.38%.
Dialog Inpainting: Turning Documents into Dialogs

Many important questions (e.g. "How to eat healthier?") require conversation to establish context and explore in depth. However, conversational question answering (ConvQA) systems have long been stymied by scarce training data that is expensive to collect. To address this problem, we propose a new technique for synthetically generating diverse and high-quality dialog data: dialog inpainting. Our approach takes the text of any document and transforms it into a two-person dialog between the writer and an imagined reader: we treat sentences from the article as utterances spoken by the writer, and then use a dialog inpainter to predict what the imagined reader asked or said in between each of the writer's utterances. By applying this approach to passages from Wikipedia and the web, we produce WikiDialog and WebDialog, two datasets totalling 19 million diverse information-seeking dialogs -- 1,000x larger than the largest existing ConvQA dataset. Furthermore, human raters judge the answer adequacy and conversationality of WikiDialog to be as good or better than existing manually-collected datasets. Using our inpainted data to pre-train ConvQA retrieval systems, we significantly advance state-of-the-art across three benchmarks (QReCC, OR-QuAC, TREC CAsT) yielding up to 40% relative gains on standard evaluation metrics.
Feature Aggregation in Zero-Shot Cross-Lingual Transfer Using Multilingual BERT

Multilingual BERT (mBERT), a language model pre-trained on large multilingual corpora, has impressive zero-shot cross-lingual transfer capabilities and performs surprisingly well on zero-shot POS tagging and Named Entity Recognition (NER), as well as on cross-lingual model transfer. At present, the mainstream methods to solve the cross-lingual downstream tasks are always using the last transformer layer's output of mBERT as the representation of linguistic information. In this work, we explore the complementary property of lower layers to the last transformer layer of mBERT. A feature aggregation module based on an attention mechanism is proposed to fuse the information contained in different layers of mBERT. The experiments are conducted on four zero-shot cross-lingual transfer datasets, and the proposed method obtains performance improvements on key multilingual benchmark tasks XNLI (+1.5 %), PAWS-X (+2.4 %), NER (+1.2 F1), and POS (+1.5 F1). Through the analysis of the experimental results, we prove that the layers before the last layer of mBERT can provide extra useful information for cross-lingual downstream tasks and explore the interpretability of mBERT empirically.