Reviewerly/exHarmony huggingface dataset:
Model for digital healthcare ecosystem based on blockchain technology: a pilot study

The aim of this study is to identify crucial factors that influence the acceptance of a digital healthcare ecosystem based on blockchain technology. Theoretical framework for the proposed model relies on the theory of stakeholders, TAM theory, and concept of readiness for changes. Statistical analysis was performed on the basis of Structural Equation Modeling (SEM). The proposed model may facilitate the use adoption of the healthcare ecosystem based on blockchain technology.
A nonlinear version of the distributed Halanay inequality and its application

In this paper, first we prove a nonlinear version of Halanay inequality with distributed delay. Then, we use it to establish a local exponential stability result for a problem appearing in neural networks theory under the name Hopfield neural network system. This is done for activation functions violating the usual standard condition of Lipschitz continuity. One of the activation functions may be non‐Lipschitz continuous. Finally, an example is provided to illustrate the developed theory.
ReSSL: Relational Self-Supervised Learning with Weak Augmentation

Self-supervised Learning (SSL) including the mainstream contrastive learning has achieved great success in learning visual representations without data annotations. However, most of methods mainly focus on the instance level information (\ie, the different augmented images of the same instance should have the same feature or cluster into the same class), but there is a lack of attention on the relationships between different instances. In this paper, we introduced a novel SSL paradigm, which we term as relational self-supervised learning (ReSSL) framework that learns representations by modeling the relationship between different instances. Specifically, our proposed method employs sharpened distribution of pairwise similarities among different instances as \textit{relation} metric, which is thus utilized to match the feature embeddings of different augmentations. Moreover, to boost the performance, we argue that weak augmentations matter to represent a more reliable relation, and leverage momentum strategy for practical efficiency. Experimental results show that our proposed ReSSL significantly outperforms the previous state-of-the-art algorithms in terms of both performance and training efficiency. Code is available at \url{this https URL}.
Application of Gamification Teaching in Primary School Fire Safety Course in Smart Learning Environment

Fire safety is a common topic that people pay close attention to every day. How can primary school students learn and master the escape skills in a short time to escape smoothly and keep their life safe when a fire breaks out? Based on the smart learning environment, this paper carries out fire safety education for primary school students by means of gamification teaching to improve their awareness of fire safety and master the skills of escape and survival.
On the Role of Optimization in Double Descent: A Least Squares Study

Empirically it has been observed that the performance of deep neural networks steadily improves as we increase model size, contradicting the classical view on overfitting and generalization. Recently, the double descent phenomena has been proposed to reconcile this observation with theory, suggesting that the test error has a second descent when the model becomes sufficiently overparameterized, as the model size itself acts as an implicit regularizer. In this paper we add to the growing body of work in this space, providing a careful study of learning dynamics as a function of model size for the least squares scenario. We show an excess risk bound for the gradient descent solution of the least squares objective. The bound depends on the smallest non-zero eigenvalue of the covariance matrix of the input features, via a functional form that has the double descent behavior. This gives a new perspective on the double descent curves reported in the literature. Our analysis of the excess risk allows to decouple the effect of optimization and generalization error. In particular, we find that in case of noiseless regression, double descent is explained solely by optimization-related quantities, which was missed in studies focusing on the Moore-Penrose pseudoinverse solution. We believe that our derivation provides an alternative view compared to existing work, shedding some light on a possible cause of this phenomena, at least in the considered least squares setting. We empirically explore if our predictions hold for neural networks, in particular whether the covariance of intermediary hidden activations has a similar behavior as the one predicted by our derivations.
Sentiment Analysis of Depression Detection on Twitter Social Media Users Using the K-Nearest Neighbor Method

Every day, millions of people suffer from depression and only a small percentage of them receive proper treatment. Depression is one of the most common mental health disorders. Mental health is very important for humans as well as physical health in general. Not infrequently media users often provide information about themselves and the complaints they experience on burdensome social media. At this time the detection can be detected from the activities of social media users themselves. Because, not infrequently Twitter social media users often provide information about themselves and the complaints they are experiencing on Twitter social media which is burdensome. Therefore, social media Twitter is an option to detect the level of mental health that is being experienced by someone. In this study, the author aims to analyze the application of the K-Nearest Neighbor method in detecting depression in Twitter social media users and see the accuracy value. Based on tests on the KNN classification using the stages of the confusion matrix, the accuracy obtained is 78.18%.
Peningkatan Kemampuan Memahami Pembacaan Cerpen Melalui Model Pembelajaran Berpikir Induktif Dengan Media Film Pendek

This study uses a classroom action research design consisting of planning, action, observation, and reflection. The research subjects were 32 students. Collecting data using direct observation, interviews, teacher and student journals and written tests. The results of the study on student activity increased from 69% in cycle 1 to 74% in cycle 2. Increased activity in PBM increased from 61.36% in cycle 1 to 80% in cycle 2. Increased student activity in carrying out evaluations of students' ability to master the material learning. This is based on the results of the evaluation of 76 in cycle 1 which increased to 81 in cycle 2. Based on the explanation above, it can be concluded that learning to understand reading short stories through an inductive thinking learning model with short film media is proven to improve the ability to understand reading short stories for class XI AKL students at SMK Negeri 3 Linggabuana Purwakarta.
An Overview of MPEG Immersive Video

MPEG immersive video is an upcoming standard for data access and delivery of immersive content. A brief introduction of its encoding and decoding processes is presented along with a simplified playback implementation of it demonstrated on Intel GPUs.
Accelerating Causal Inference and Feature Selection Methods through G-Test Computation Reuse

This article presents a novel and remarkably efficient method of computing the statistical G-test made possible by exploiting a connection with the fundamental elements of information theory: by writing the G statistic as a sum of joint entropy terms, its computation is decomposed into easily reusable partial results with no change in the resulting value. This method greatly improves the efficiency of applications that perform a series of G-tests on permutations of the same features, such as feature selection and causal inference applications because this decomposition allows for an intensive reuse of these partial results. The efficiency of this method is demonstrated by implementing it as part of an experiment involving IPC-MB, an efficient Markov blanket discovery algorithm, applicable both as a feature selection algorithm and as a causal inference method. The results show outstanding efficiency gains for IPC-MB when the G-test is computed with the proposed method, compared to the unoptimized G-test, but also when compared to IPC-MB++, a variant of IPC-MB which is enhanced with an AD-tree, both static and dynamic. Even if this proposed method of computing the G-test is presented here in the context of IPC-MB, it is in fact bound neither to IPC-MB in particular, nor to feature selection or causal inference applications in general, because this method targets the information-theoretic concept that underlies the G-test, namely conditional mutual information. This aspect grants it wide applicability in data sciences.
Comprehensive Punctuation Restoration for English and Polish

Punctuation restoration is a fundamental requirement for the readability of text derived from Automatic Speech Recognition (ASR) systems.Most contemporary solutions are limited to predicting only a few of the most frequently occurring marks, such as periods, commas, and question marks -and only one per word.However, in written language, we deal with a much larger number of punctuation characters (such as parentheses, hyphens, etc.), and their combinations (like parenthesis followed by a dot).Such comprehensive punctuation cannot always be unambiguously reduced to a basic set of the most frequently occurring marks.In this work, we evaluate several methods in the comprehensive punctuation reconstruction task.We conduct experiments on parallel corpora of two different languages, English and Polish -languages with a relatively simple and complex morphology, respectively.We also investigate the influence of building a model on comprehensive punctuation on the quality of the basic punctuation restoration task.
Online Educational Platform as a Web Content Management System in the Organization of Student-Teacher Interaction

The ubiquity of the Internet has led to a change in the ways of knowledge transfer and communication on the Web, which have become more intensive and focused on social interactions. Educational institutions have at their disposal various online platforms and tools for organizing online learning. But today it is becoming important to develop online educational platforms that take into account the specifics of educational institutions so that they meet their needs and requirements. The article analyzes the concept of "online educational platforms" and their features. The possibilities for educational institutions to organize the training process in the face of networking growth based on online platforms and Internet services are discussed. The capabilities of the developed online educational platform are presented, including various functions to create online learning courses, solve administrative tasks, and implement network interaction among participants of the learning process so that it meets the needs of teachers and students. All the tools of the developed online platform are easy to use by teachers of various academic disciplines and allow them to quickly adjust and supplement the online courses they develop themselves; they also enable to organize effective training and interaction between students and teachers, implement knowledge control, exchange pedagogical experience, and make it easier for students to create their own projects on the online platform.
Application of Data Mining to Predict the Sales of the Best-Selling Shoe Products using the Simple Linear Regression Method

Sales prediction is an estimate of sales at a future time in certain circumstances and is made based on data that has occurred. This prediction is influenced by the sales of products in PT.Sepatu Bata. Simple Linear Regression is a linear relationship between one independent variable and the dependent variable, this analysis is to determine the direction of the relationship between the independent variable and the dependent variable whether it is positive or negative and to predict the value of the dependent if the value of the independent variable has increased or decreased. The author will design a data mining implementation system to predict the sales of the best-selling shoe products to better utilize existing sales transaction data. The design will be implemented using the PHP programming language and MySQL database. This research is expected to produce a datamining implementation system to predict the sales of the best-selling shoe products using the website-based simple linear regression method.
Predictability of Delayed Visual Feedback Under Rubber Hand Illusion Modulates Localization but Not Ownership of the Hand

The rubber hand illusion (RHI) is a perceptual illusion, whereby a fake hand is recognized as one's own hand when a fake hand and felt real hand are stroked synchronously. RHI strength is mainly assessed using a questionnaire rating and proprioceptive drift (PD). PD is characterized by the proprioceptively sensed location of the participant's own hand shifting toward the location of the fake hand after RHI. However, the relationship between the two measures of hand ownership and location remains controversial due to mixed findings: some studies report correlations between them, while others show that they are independent. Here, we demonstrated significant PD without RHI using delayed visual feedback. In this RHI study, video images of the fake hand were delivered to the subjects, and four delay intervals of visual feedback (80, 280, 480, and 680ms) were introduced. In four of six conditions, the delay interval was fixed throughout the condition. In the other two conditions, four delays were delivered in a predetermined order (i.e., serial condition; higher predictability) or in a pseudo-random order (i.e., random condition; low predictability). For the four conditions with a fixed delay, the questionnaire ratings and PD declined significantly when the delay interval exceeded circa 300ms. In both the serial and random conditions, no illusory ownership of the fake hand was reported in the questionnaire. In contrast, greater PD was found in the random condition but not in the serial condition. Our findings suggest that hand ownership and localization are caused by distinct multisensory integration processes.
Stein Variational Recommendation System with Knowledge Embedding Enabling the IoT Services

The Internet of Things (IoT) interconnects various devices and services, of which recommendation services are an important component to help the development of IoT applications. Furthermore, without the aid of suitable online recommendation systems, Internet users will be overwhelmed by the tremendous amount of contents. Researchers have thus developed a large volume of recommendations. However, they are all flawed with high complexity, cold start issues, inability to generalize, etc. In recent years, some researchers had turned to variational inference (VI)-based recommendation systems, which can solve the above problems to some extent. However, these VI-based recommendations are merely hybrid methods of VI with the existing recommendation algorithms and are unable to be implemented well in real practices. Therefore, developing algorithms that can overcome these limitations of the existing online recommendation systems is essential for convenient and useful Internet searches. In this paper, we propose, develop, implement and test a more general, new and innovative Stein Variational Recommendation System algorithm (SVRS) to tackle the long plaguing recommendation problems. Based on Stein’s identity, the SVRS algorithm can compute the feature vector of existing users and items it had rated, and further predict the ratings for users that have not been engaged with certain content. SVRS provides more general insights into the forming of user ratings, can be easily extended to higher dimensions and has the merits of low complexity, easy scaling and generalizability. Experiments show that SVRS outperforms the other existing type of recommendation algorithms and it has higher accuracy in terms of mean absolute error (MAE) and root mean square error (RMSE).
Audio feature optimization approach towards speaker authentication in banking biometric system

Experiments were carried out using algorithms such as Principal Component Analysis, Feature Importance, and Recursive Parameter Elimination dentifying most meaningful Mel-Frequency Cepstral Coefficients representing speech excerpts prepared for their classification are presented and discussed. The parameterization was made using Mel Frequency Cepstral Coefficients, Delta MFCC and Delta MFCC. In the next stage, feature vectors were passed to the input of individual algorithms utilized to reduce the size of the vector by previously mentioned algorithms. The vectors prepared in this way have been used for classifying vocalic segments employing Artificial Neural Network (ANN) and Support Vector Machine (SVM). The classification results using both classifiers and methods applied for reducing the number of parameters were presented. The results of the reduction are also shown explicitly, by indicating parameters proven to be significant and those rejected by particular algorithms. Factors influencing the obtained results were considered, such as difficulties associated with obtaining the data set, and ts labeling. The broader context of banking biometrics research carried-out and the results obtained in this domain were also discussed. [Project No. POIR.01.01.01-0092/19 entitled: “BIOPUAP—A biometric cloud authentication system” is currently financed by the Polish National Centre for Research and Development (NCBR) from the European Regional Development Fund.]
A Novel 3D Convolutional Neural Network for Action Recognition in Infrared Videos

As Infrared (IR) imaging is sensitive to objects emitting heat, it is more useful to distinguish people from the background compared with visible spectrum imaging, especially in poor light enviornment. Recently, IR images have attracted increasing attention in action recognition, for which Convolutional Neural Networks (ConvNets) have achieved great success in both aspects of performance and speed. Compared to 2D ConvNets, 3D ConvNets are more powerful tools for action recognition which can jointly extract features from temporal and spatial domains and combine these together to enhance the performance. In this paper, we propose a novel 3D ConvNet with a deep architecture to realize action recognition in IR videos. Besides, a residual fully connected (FC) module is introduced after the ConvNet backbone to improve the performance. Furthermore, we employe a transfer learning strategy, i.e., the proposed 3D ConvNet is pretrained on a large-scale visible spectrum dataset and then finetuned with false-color version of IR images to generalize well in the action recognition task. Experimental results demonstrate the superiority of the proposed method in Average Precision comparisons.
Self - Organizing Teams in Software Development – Myth or Reality

In an agile business environment, teamwork quality is underlining key element of successful projects and overall team performance. In the Scrum framework, self-managing team is a central element of success. Self-managing teams introduced a new concept of teams and emphasized the importance of individual member effort and the way it affects team performance. The focus of this paper is on teamwork quality in self-managing teams and the way it affects the overall team performance. Teamwork quality is defined by six subconstructs: communication, coordination, balance of member contributions, mutual support, effort, and cohesion, while team performance is defined with effectiveness and efficiency. A survey of 260 respondents from the IT sector in Bosnia and Herzegovina was conducted and in both tested models, our results have confirmed that the highest effect on team effectiveness comes from mutual support, with slightly different – but still significant, impact of team cohesion and effort.
Pseudo-siamese networks with lexicon for Chinese short text matching

Short text matching is one of the fundamental technologies in natural language processing. In previous studies, most of the text matching networks are initially designed for English text. The common approach to applying them to Chinese is segmenting each sentence into words, and then taking these words as input. However, this method often results in word segmentation errors. Chinese short text matching faces the challenges of constructing effective features and understanding the semantic relationship between two sentences. In this work, we propose a novel lexicon-based pseudo-siamese model (CL2 N), which can fully mine the information expressed in Chinese text. Instead of utilizing a character-sequence or a single word-sequence, CL2 N augments the text representation with multi-granularity information in characters and lexicons. Additionally, it integrates sentence-level features through single-sentence features as well as interactive features. Experimental studies on two Chinese text matching datasets show that our model has better performance than the state-of-the-art short text matching models, and the proposed method can solve the error propagation problem of Chinese word segmentation. Particularly, the incorporation of single-sentence features and interactive features allows the network to capture the contextual semantics and co-attentive lexical information, which contributes to our best result.
Accurate Identification of Agricultural Inputs Based on Sensor Monitoring Platform and SSDA-HELM-SOFTMAX Model

The unreliability of traceability information on agricultural inputs has become one of the main factors hindering the development of traceability systems. At present, the major detection techniques of agricultural inputs were residue chemical detection at the postproduction stage. In this paper, a new detection method based on sensors and artificial intelligence algorithm was proposed in the detection of the commonly agricultural inputs in Agastache rugosa cultivation. An agricultural input monitoring platform including software system and hardware circuit was designed and built. A model called stacked sparse denoising autoencoder-hierarchical extreme learning machine-softmax (SSDA-HELM-SOFTMAX) was put forward to achieve accurate and real-time prediction of agricultural input varieties. The experiments showed that the combination of sensors and discriminant model could accurately classify different agricultural inputs. The accuracy of SSDA-HELM-SOFTMAX reached 97.08%, which was 4.08%, 1.78%, and 1.58% higher than a traditional BP neural network, DBN-SOFTMAX, and SAE-SOFTMAX models, respectively. Therefore, the method proposed in this paper was proved to be effective, accurate, and feasible and will provide a new online detection way of agricultural inputs.
English Meeting Club: Students’ Perception and Their Speaking Skill

This study aims to find out students’ perception of English meeting club to support their speaking skill. English meeting club was a club or group of humans with common ambitions or interests in English. This study focused on the students’ perception of English meeting club to support their speaking skill. The subject of this study are 10 students of English department as participants. This study used a qualitative method, this study used interview as research instrument. The result of interviews, the majority of the students had positive perception about joined in English club. The student absolutely believes if English club could support them to speak English fluently. The student also had a perception through an English meeting club as a media to develop skills particularly in speaking, it could be one of the alternatives for those who need to learn and practice more about how to speak English well and it can also enhance their ability to speak English fluently.