Reviewerly/exHarmony huggingface dataset:
Early Childhood Preservice Teacher’s Perception on Play: Focusing on Concept Mapping Analysis

The purpose of this study is to examine early childhood preservice teacher’s perception on play using the concept mapping analysis. Multidimentional scale analysis and hierarchical cluster analysis were performed. The results of this study can be summarized as follows. 47 final statements were finally generated based on the perception on play, and were classified into two dimensions of “the essence of play-play support” and “the educational significance of play-the developmental importance of play”. In addition, the statements were classified into five clusters: ‘diversity of play’, ‘aspects of play’, ‘role of teacher’, ‘necessity of play in young children’, and ‘value of play’. This study provides implication for support necessary for early childhood preservice teachers or the direction of early childhood teacher education course to form the desirable perception on play.
Information Storage and Retrieval System: an analysis of the impact of variables and measures aimed at the organization and retrieval of information centered on the user

Introduction:The effective performance of an Information Retrieval System depends on the quality with which the organization of information is performed, which will imply a retrieval of the most relevant and pertinent information, since these procedures are conditioned to each other, creating a bridge between input and output of information. Objective:To evaluate the impact of the exhaustiveness and specificity variables and the recall and precision measures, as well as the concepts of relevance and pertinence, in Information Retrieval Systems. Methodology:It is characterized as a descriptive and exploratory study, based on a narrative literature review aiming to present the different concepts, their converging and divergent points. Results:As a contribution, we present a proposal for a flow for an Information Storage and Retrieval System, centered on the user, bringing together several aspects related to measures of recall and precision, of relevance and pertinence.Conclusion:It is considered as the final contribution of this study to highlight the importance of a systemic view, in which all elements of an Information Storage and Retrieval System are in interrelation, having the user as the main element; and present the fundamental activities that are important for the training of professionals able to build consistent Systems.
Syclus planning ethanol-to-ethylene

The start-up Syclus has chosen technology from the French engineering firm Axens for an ethanol-to-ethylene plant it is planning in Chemelot industrial park, in Geleen, the Netherlands. The plant will have capacity for about 100,000 metric tons per year of ethylene, the quality of which will be suitable for the region’s ethylene pipeline network. CropEnergies, a German firm that owns a 50% stake in Syclus, will supply ethanol to the plant. Syclus estimates that the plant will cost $145 million to build and come on line in 2026.
Research on Intelligent Language Recognition System Based on Computer Big Data Technology

This paper studies language recognition in time domain and frequency domain. In this paper, the powerful mathematical calculation function of MATLAB is used to identify the isolated sound. In this paper, HMM is used as the identification method. The speech recognition based on MFCC is realized by using MFCC as the basic characteristic parameter of Chinese speech. The main functions include sound preprocessing, feature parameter extraction, recognition template training, recognition and matching algorithm. The environment design of speech recognition based on MATLAB GUI is given. The identification performance of the proposed method is verified by simulation.
Status and role positions of the vice-rector as a subject of changes in the youth policy and educational activities of the university

This article reflects changes in the field of higher education aimed at creating a unified system of education of students based on traditional spiritual and moral values and social solidarity of the Russian society. Despite the large number of scientific publications and discourse directed by the active activities of public authorities and the university community, there has not been a systematic understanding of professional competencies and personal characteristics of the main participants and initiators of these changes at the level of higher education institutions. It is primarily about the status and role positions of vice-rectors for youth policy and educational activities in the ongoing changes, as well as determining the totality of their personal qualities and competencies for the effective performance of their functions in the higher education. The research is based on the activity-activist approach, which provides theoretical and methodological tools in considering the specifics and content of the activities of actors as the elements of mechanism of change. The results obtained in the course of the study are of interest for optimizing the socio-professional activities of vice-rectors for the youth policy and educational work in the higher education organizations.
Power Side-channel Attack Resistant Circuit Designs of ARX Ciphers Using High-level Synthesis

In the Internet of Things (IoT) era, edge devices have been considerably diversified and are often designed using high-level synthesis (HLS) for improved design productivity. However, HLS tools were originally developed in a security-unaware manner, resulting in vulnerabilities to power side-channel attacks (PSCAs), which are a serious threat to IoT systems. Currently, the impact and applicability of existing methods to PSCA-resistant designs using HLS are limited. In this article, we propose an effective HLS-based design method for PSCA-resistant ciphers implemented in hardware. In particular, we focus on lightweight block ciphers composed of addition/rotation/XOR (ARX)-based permutations to study the effects of the threshold implementation (which is one of the provably secure countermeasures against PSCAs) to the behavioral descriptions of ciphers along with the changes in HLS scheduling. The results obtained using Welch’s t-test demonstrate that our proposed method can successfully improve the resistance against PSCAs for all ARX-based ciphers used as benchmarks.
Research on the impact of AI on college students based on the entropy weight integrated scoring model

In recent years, artificial intelligence technology is developing continuously, which has had a wide impact on the study of college students. This paper uses scale, independent heat coding and other methods to quantify the questionnaire data, screen the evaluation index system by establishing gradual regression model, correlation model and comprehensive evaluation model of entropy right, and quantify the influence of artificial intelligence on college students' learning. First of all, the raw data was statistically analyzed and visualized. The analysis showed that 93.2% of college students hoped to actively use artificial intelligence tools to improve their academic performance. Then, the influence of artificial intelligence on the learning of college students was studied, and the evaluation index system was determined through the Pearson correlation model and the stepwise regression model. Finally, the effect of AI on undergraduate learning is studied through the entropy weight integrated scoring model.
Comparative Analysis of Path Planning Algorithms and Prospects for Practical Application

Path planning, as an important applications of artificial intelligence technology in the field of intelligent transportation, has a wide range of application prospects in achieving efficient, intelligent, and safe travel. This paper first introduces the importance of path planning technology in the field of transportation, and then discusses the development of path planning from two aspects: traditional methods and new methods based on artificial intelligence. In terms of traditional methods, the principles and advantages and disadvantages of methods such as Dijkstra's algorithm, A* algorithm, and Floyd's algorithm are included. In terms of new methods based on artificial intelligence, the principles and application examples of methods such as genetic algorithms, simulated annealing algorithms, and deep reinforcement learning are included. Finally, the problems existing in path planning field are analyzed, and the future research directions are prospected in order to provide useful references for the development of intelligent transportation.
Influence of supportive leadership style on the innovative behavior of senior managers in the manufacturing sector in Kenya

This study sought to determine the influence of supportive leadership style on the innovative behaviorof senior managers in the manufacturing sector in Kenya. Positivism research philosophy anddescriptive correlational research design were used. The target population comprised of 802 seniormanagers in manufacturing firms in Kenya. Stratified random sampling method was used to determinea sample size of 267 senior managers from the total population. The ordinal logistic regression(Nagelkerke Pseudo R-square) results revealed that supportive leadership style explained 6.6% of thevariance in innovative behavior (R2 = .066). Goodness-of-fit results showed that the observed data wasconsistent with the fitted model, [X2 (28, N = 228) = .564, p=1.00, p&gt;.05]. Results of the parameterestimates revealed that supportive leadership style positively and significantly predicted innovativebehavior (β =.611, p=.000, p&lt;.05). Based on these results, the null hypothesis that supportiveleadership style does not significantly influence the innovative behavior of senior managers wasrejected. This study concludes that supportive leadership style significantly influenced the innovativebehavior of senior managers in the manufacturing sector in Kenya. The findings of this study willprovide a better understanding and new knowledge to the players in the manufacturing sector on theinfluence of supportive leadership style on the innovative behavior of senior managers, to improve theirperformance and competitiveness. The study recommends that further research should be conducted onthe influence of supportive leadership on innovative behavior in the small-scale manufacturingorganizations in Kenya
Weighted-type image segmentation model via coupling heat kernel convolution with high-order total variation

Image segmentation is an essential step for many applications in the field of the image analysis.One of the main challenges for this task is how to accurately locate complicated boundary and properly segment a region of interest efficiently.To this end, this paper provides a new scheme by combining the adaptive weight function and the high-order total variation term to improve the robustness of the classical active contour model.In order to reduce the computational complexity, our model uses the heat kernel convolution with adaptive weight to approximate the perimeter of the segmentation area.Due to the nonsmoothness of the proposed model, we adopt the alternating direction method of multipliers to solve it.Numerical implementations on several different types of images illustrate that our proposed scheme demonstrates better segmentation performance and robustness than several existing state-of-theart segmentation models.
Drop-DIP: A single-image denoising method based on deep image prior

Over the past few years, deep learning methods have emerged as powerful image denoising tools.Among them, unsupervised deep learning without external training data is more practical and challenging.Reducing noisy overfitting is challenging due to single-image unsupervised learning is prone to overfitting.In this paper, we propose a method named drop-DIP combing Deep Image Prior (DIP) with drop-out for the first time to solve the above problems.In our method, we construct new network training pairs by performing drop-out training on the Bernoulli sampling of the input and output, and then construct a regularization term by using the corrected bias of the output and the generated prior.Finally, update the parameters through the Alternating Direction Method of Multipliers (ADMM) algorithm.Experiments demonstrate that drop-DIP can alleviate the overfitting difficulty in DIP, facilitate the early stopping of the network, and is applicable to different noise models.Furthermore, our method has good performance on Peak Signal to Noise Ratio (PSNR), Structural Similarity (SSIM), and Learned Perceptual Image Patch Similarity (LPIPS) metrics validated by two different datasets.
Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation Prediction

Temporal relation prediction in incomplete temporal knowledge graphs (TKGs) is a popular temporal knowledge graph completion (TKGC) problem in both transductive and inductive settings. Traditional embedding-based TKGC models (TKGE) rely on structured connections and can only handle a fixed set of entities, i.e., the transductive setting. In the inductive setting where test TKGs contain emerging entities, the latest methods are based on symbolic rules or pre-trained language models (PLMs). However, they suffer from being inflexible and not time-specific, respectively. In this work, we extend the fully-inductive setting, where entities in the training and test sets are totally disjoint, into TKGs and take a further step towards a more flexible and time-sensitive temporal relation prediction approach SST-BERT,incorporating Structured Sentences with Time-enhanced BERT. Our model can obtain the entity history and implicitly learn rules in the semantic space by encoding structured sentences, solving the problem of inflexibility. We propose to use a time masking MLM task to pre-train BERT in a corpus rich in temporal tokens specially generated for TKGs, enhancing the time sensitivity of SST-BERT. To compute the probability of occurrence of a target quadruple, we aggregate all its structured sentences from both temporal and semantic perspectives into a score. Experiments on the transductive datasets and newly generated fully-inductive benchmarks show that SST-BERT successfully improves over state-of-the-art baselines.
Ensemble Modeling with Contrastive Knowledge Distillation for Sequential Recommendation

Sequential recommendation aims to capture users' dynamic interest and predicts the next item of users' preference. Most sequential recommendation methods use a deep neural network as sequence encoder to generate user and item representations. Existing works mainly center upon designing a stronger sequence encoder. However, few attempts have been made with training an ensemble of networks as sequence encoders, which is more powerful than a single network because an ensemble of parallel networks can yield diverse prediction results and hence better accuracy. In this paper, we present Ensemble Modeling with contrastive Knowledge Distillation for sequential recommendation (EMKD). Our framework adopts multiple parallel networks as an ensemble of sequence encoders and recommends items based on the output distributions of all these networks. To facilitate knowledge transfer between parallel networks, we propose a novel contrastive knowledge distillation approach, which performs knowledge transfer from the representation level via Intra-network Contrastive Learning (ICL) and Cross-network Contrastive Learning (CCL), as well as Knowledge Distillation (KD) from the logits level via minimizing the Kullback-Leibler divergence between the output distributions of the teacher network and the student network. To leverage contextual information, we train the primary masked item prediction task alongside the auxiliary attribute prediction task as a multi-task learning scheme. Extensive experiments on public benchmark datasets show that EMKD achieves a significant improvement compared with the state-of-the-art methods. Besides, we demonstrate that our ensemble method is a generalized approach that can also improve the performance of other sequential recommenders. Our code is available at this link: https://github.com/hw-du/EMKD.
Decision making software for cosmetic product design based on an ontology

The design of formulated chemical products such as cosmetic emulsions is normally carried out by interdisciplinary teams that need versatile and reliable tools to support them in the decision-making process. In view of this, this article presents a tool to support product designers during the conceptual design of emulsified cosmetic products. The tool is based on a previously developed ontology for cosmetic product design. It integrates concepts from emulsion science, cosmetic formulation, expert knowledge, and design heuristics in a systematic and flexible way. Additionally, it has a modern architecture based on mobile applications which makes it easy to use. The tool support three types of decisions: screening of ingredients, selection of ingredients considering multiple criteria (ingredient performance, origin, price, etc.), and evaluation of a formulation according to design heuristic. As a result, the tool allows the visualization of ingredient properties and a better selection of ingredients, as well as an early evaluation of formulations prior to experimentation.
SAppKG: Mobile App Recommendation Using Knowledge Graph and Side Information-A Secure Framework

Due to the rapid development of technology and the widespread usage of smartphones, the number of mobile applications is exponentially growing. Finding a suitable collection of apps that aligns with users’ needs and preferences can be challenging. However, mobile app recommender systems have emerged as a helpful tool in simplifying this process. But there is a drawback to employing app recommender systems. These systems need access to user data, which is a serious security violation. While users seek accurate opinions, they do not want to compromise their privacy in the process. We address this issue by developing SAppKG, an end-to-end user privacy-preserving knowledge graph architecture for mobile app recommendation based on knowledge graph models such as SAppKG-S and SAppKG-D, that utilized the interaction data and side information of app attributes. We tested the proposed model on real-world data from the Google Play app store, using precision, recall, mean absolute precision, and mean reciprocal rank. We found that the proposed model improved results on all four metrics. We also compared the proposed model to baseline models and found that it outperformed them on all four metrics.
Noise Resilience of Reduced Precision Neural Networks

Reduced Precision Neural Networks, where computations are performed with as low as one or two bits of precision, are starting to find relevance in a wide range of applications, including vision, speech, and natural language processing. Such networks are capable of running on low power and cost on embedded systems such as FPGAs (field programmable gate arrays). Recent research has extensively studied and advanced the accuracy of these networks. However, unlike regular neural networks, little is known about how resilient they are in the presence of noisy input data. From old photographs that are rediscovered when you dig through your attic to images taken from thousands of miles away in space, noisy input data is a common factor in everyday life. In this study, we characterize the behavior of Reduced precision neural networks to noisy input data and identify techniques to improve their resilience. Benchmark image data is injected with different noise profiles, and the inference capabilities of reduced-precision networks (based on Yolo, Dorefa-net) are studied and contrasted with full precision neural networks. Experimental results show that reduced-precision networks perform well, within 1-5% accuracy, relative to full precision networks in the presence of significant levels of noise. We also show that significant improvements () to overall image recognition accuracy are possible to achieve by creating a high-quality ensemble neural network, which combines multiple reduced-precision neural networks.
Multivariate Time-series Data Correction by combining Attention-based LSTM and GAN Model

High-quality data can increase the reliability of machine learning-based prediction models. In our work, we propose a novel method for data correction to improve the quality of multivariate time-series data. For this, we use a LSTM-based VAE-GAN for anomaly detection and an Attention-based LSTM model for data correction. Through experiments using Secure Water Treatment (SWaT) data, we show that the proposed correction method is superior to previous correction methods.
Probability and Probability Distributions

The probability theory is considered the foundation of statistics, and R language has capability of working with probability, random variables, and probability distributions. This chapter addresses how to use R when dealing with elementary probability and related problems for a finite sample space. A variety of examples have been provided to illustrate many popular discrete and continuous probability distributions. Plotting of the distributions and generation of random variables have also been provided. This chapter also touches the use of two latest packages available for R to make new distributions and study other properties, namely “distr” and “distrEx”.
Graphing in R 

In Chap. 6 Sect. 6.3 , we discussed high level graphical functions from the base package.
Functions for Better Programming

Better computer programming involves writing code that is not only functional but also easy to read, understand, and maintain. This requires adhering to best practices such as writing clean and concise code, commenting appropriately, and organizing code in a structured manner. Furthermore, better computer programming also involves utilizing appropriate functions. This chapter discusses how to use important R functions to enhance user’s programing skills and computational capabilities. The primary focus of the chapter lies in the functions that improve the R output display and format.
Machine Learning-Based Hybrid Movie Recommendation System

Recommendation systems play a crucial role in providing personalized and relevant experiences for users. By analyzing enormous amounts of data, a movie recommendation system can provide users with an efficient way to find the films that best suit their tastes. In this study, we provide a movie recommendation system that uses a hybrid of content-based and collaborative filtering approaches to generate more reliable suggestions. As opposed to content-based model, which uses movie attributes like genre, director, and actor to make recommendations, collaborative filtering utilizes the preferences and opinions of users who share the current user's tastes and interests to recommend movies which are similar to those already shown interest in by the current user. By combining these two approaches, a hybrid movie recommendation system provides a more comprehensive and personalized recommendation experience. The hybrid model is shown to have better performance and accuracy than the separate models.