Reviewerly/exHarmony huggingface dataset:
Localized multiple kernel learning using graph modularity

Multiple kernel learning (MKL) algorithms exploit information from multiple feature representations by assigning weights to each representation in the kernel space, and later combining them. However, this ignores the fact that data points exhibit locally varying characteristics. To address this problem, localized MKL algorithms learn locality-specific kernel weights which determine each base kernel’s influence in the locality under consideration. Here, we relate the problem of determining the relevance of base kernels for classification to that of quantifying community structure in graphs. Next, we derive sample-specific kernel weights using graph modularity. Through experiments on publicly available datasets, we show that the proposed approach offers a viable alternative to state-of-the-art MKL approaches while being computationally inexpensive.
Design of a deep learning visual system for the thickness measurement of each coating layer of TRISO-coated fuel particles

In the new generation of nuclear energy system, the thickness of the coating layer of tristructural isotropic (TRISO)-coated fuel particles is one of the most important parameters. Recently, some visual-based methods have been developed for the thickness measurement of each coating layer, but the existing method still lacks of practicality. In this study, an advanced visual system combined with the ceramographic section method and deep learning algorithms is designed to automatically measure the thickness values of each coating layer. In the designed visual system, an automatic image acquisition method is first achieved. After that, an accurate thickness measurement method is proposed based on the designed image segmentation model. Finally, to enhance the reliability and consistency of the measurement results, a tracing method is developed for the designed measurement system. The experimental results demonstrate that the designed system can accurately automatically measure the thickness values of each coating layer.
Differentially private frequent episode mining over event streams

Frequent episode mining is a wide range framework of data mining from sequential data with many applications, which is a totally short-ordered collection of event-types and unearths temporal correlations without information loss over event streams. While offering substantial benefits, directly releasing frequent episodes to the public will enormously threaten the individual's privacy. However, there is little work so far concentrating on privately frequent episode mining. In this paper, we investigate the privacy problem in mining frequent episodes from event streams due to continuous releases in successive windows and propose a real-time differentially private frequent episode mining algorithm over event streams to avoid the privacy leakage with ω-event privacy guarantee. To obtain private frequent episodes, we propose a sample-based perturbation approach, which improves the accuracy of selecting frequent episodes based on sampling databases. To reduce the privately mining time and avoid repeatedly privacy budget allocation to coincident window of adjacent releases as much as possible, we present an incremental perturbation approach according to the judgment in dissimilarity calculation mechanism. Meanwhile, in order to protect data collected from any ω successive timestamps over event streams, we employ an adaptive ω-event privacy mechanism on the basis of the dynamicity of episodes. Finally, experimental results on real-world datasets demonstrate the effectiveness and efficiency of our algorithm.
Selection of Optimal Thresholds in Multi-Level Thresholding Using Multi-Objective Emperor Penguin Optimization for Precise Segmentation of Mammogram Images

In medical image examination, image segmentation is the broadly used method. Currently, the efficient segmentation of mammogram images is the main challenge. Many methods were presented for segmenting the mammogram images, but the results are not satisfactory. In this paper, an efficient segmentation of mammogram images-based Multilevel Thresholding (MLT) method is proposed. Initially, the preprocessing step is executed for eliminating the unnecessary noises. For gaining the useful features from the mammogram images, mammogram image segmentation is carried out using multilevel thresholding method. In this paper, a novel Multi-Objective Emperor Penguin Optimization (MOEPO) algorithm is proposed for searching the multilevel greatest thresholds that segment the images into background and objects. The objective functions of the MLT are Otsu’s method, Kapur and Tsallis entropy. The effectiveness of the proposed method is analyzed using several performances evaluating metrics, like PSNR, FSIM and SSIM. The experimental outcomes show that the performance of the proposed technique is superior to other state-of-the-art methods. The proposed technique is likened to three existing models, viz. ScPSO-MT, Double Threshold and IWO-SUSAN. The SSIM of the proposed technique is 24.99%, 27.83% and 26.95% better than ScPSO-MT, Double Threshold and IWO-SUSAN existing approaches. The PSNR of the proposed technique is 25.27%, 40% and 50.74% better than ScPSO-MT, Double Threshold and IWO-SUSAN approaches. The FSIM of the proposed technique is 28.57%, 34.12% and 34.12% better than ScPSO-MT, Double Threshold and IWO-SUSAN methods.
Effectiveness of YouTube as Learning Media in Improving Learners’ Speaking Skills

In language learning, speaking becomes the evidence of students’ competence of the language that is learnt. However, students’ speaking ability can be inhibited by several problems that are faced such as lack of confidence, lack of vocabulary and grammar, and inappropriate teaching strategy. In this era, the developments of information and technology (IT) can be the solution of those problems. The development of technology seems to have many positive effects on education. Technology provides a variety of learning media that is fun for students. One of the technological developments that can be used as learning media is YouTube. YouTube provides various types of videos related to learning. In learning English, EFL learners can find various types of videos that can help the learning process. Therefore, YouTube can help learners to improve their language skill, especially their speaking skills. The aim of this paper is to explain the effectiveness of using YouTube as learning media in improving learner’s speaking skills
Application of Augmented Reality Technology in Children’s Picture Books Based on Educational Psychology

To cultivate children's imagination, observation, thinking ability, and aesthetic consciousness, the questionnaire survey is adopted to analyze the design strategies and principles of children's picture books based on augmented reality (AR). Primarily, the related concepts and theories are expounded for the research content. Children in preschool aged 4-5 years are invited as primary participants in this work, and the psychological characteristics of the invited children are analyzed in depth. Then, a study is carried out on the existing AR children's picture books. The problems existing in the design of AR children's picture books are found, and then, related solutions are put forward based on the results of the questionnaire survey. Besides, a design is made on the strategies and interactive design principles of AR children's picture books on mobile terminals that are more in line with the needs of children. The results show that 41.07% of parents do not understand AR technology, and 37.5% of preschool children indicate that they do not operate mobile devices independently. However, they need the assistance of parents to use this kind of picture book. A total of 44.64% of parents believe that the main problem of AR picture books in the current market is the lack of interesting interaction. Given the above problems, five principles are proposed for the design of AR children's picture books based on mobile terminals, namely, easy operation principle, interesting principle, guiding principle, timely feedback principle, and safety principle. A set of universally applicable design methods are proposed for AR children's picture books based on mobile terminals, which provides certain theoretical guidance for the development of related types of products.
Interleaver Design and Pairwise Codeword Distance Distribution Enhancement for Turbo Autoencoder

This paper enhances the performance and training of the Turbo Autoencoder (TurboAE), an end-to-end jointly trained neural channel encoder and decoder. A novel interleaver for TurboAE with convolutional neural network (CNN) is proposed, which is shown to enhance the end-to-end performance of both TurboAE continuous and binary. Thanks to the proposed interleaver, TurboAE binary performs better than a benchmark Turbo code of length 100. In addition, a novel additional term to the loss function is proposed to improve the pairwise distance distribution of the codewords, which is shown to help the training convergence of TurboAE with CNN and recurrent neural network (RNN).
Convex Vision-Based Negative Obstacle Detection Framework for Autonomous Vehicles

This study investigates the convex optimization frameworks for accurate and fast negative obstacle detection as well as depth estimations that are vital for real-time, superior autonomous vehicle operations. A convex framework is proposed in conjunction with a unique stereo perception approach to manage the complex nature of such invisible obstacles. The proposed stereo configuration incorporates an alignment that displaces the cameras along a vertical baseline to extract useful information pertaining to negative obstacle features for various illustrative terrain settings. The convex framework exploits these properties to evaluate depth jumps in the disparity space image and perform geometrical analysis of potential occlusion regions. The corresponding convex formulations are based on linear matrix inequalities to detect sudden disparity, angle profile, and intensity variations for potential negative obstacles and to estimate the internal depth of the detected obstacles. Experimental results demonstrate that consecutive processes can be structured in a convex framework to efficiently identify negative obstacle attributes at a range of distances for texture varying environments, providing a vital extension to real-time vehicular system implementations with superior detection rates.
A Demonstration of Human-Robot Communication Based on Multiskilled Language-Image Analysis

Successful human-robot communication relies on strong machine-learning capabilities of natural language processing with contextual constraints. This paper developed and tested a communication platform using a social robot to investigate the applications of multiskilled language-image analysis. Three users tested the system to successfully complete a protocol based on referential communication tasks. The system may have broad applications in various scenarios involving human robot interaction.
An Efficient Method for Comparing Numbers and Determining the Sign of a Number in RNS for Even Ranges

Fully Homomorphic Encryption (FHE) permits processing information in the form of ciphertexts without decryption. It can ensure the security of information in common technologies used today, such as cloud computing, the Internet of Things, and machine learning, among others. A primary disadvantage for its practical application is the low efficiency of sign and comparison operations. Several FHE schemes use the Residue Number System (RNS) to decrease the time complexity of these operations. Converting from the RNS to the positional number system and calculating the positional characteristic of a number are standard approaches for both operations in the RNS domain. In this paper, we propose a new method for comparing numbers and determining the sign of a number in RNS. We focus on the even ranges that are computationally simple due to their peculiarities. We compare the performance of several state-of-art algorithms based on an implementation in C++ and relatively simple moduli with a bit depth from 24 to 64 bits. The experimental analysis shows a better performance of our approach for all the test cases; it improves the sign detection between 1.93 and 15.3 times and the number comparison within 1.55–11.35 times with respect to all the methods and configurations.
Quantitative mapping of dolomitization using close-range hyperspectral imaging: Kimmeridgian carbonate ramp, Alacón, NE Spain

Abstract Geological models from outcrop analogues are often utilized as a guide, or soft constraint, for distributing reservoir properties in subsurface models. In carbonate outcrops, combined sequence stratigraphic, sedimentological, and petrographic studies constrain the heterogeneity of geobodies and diagenetic processes, including dolomitization, at multiple scales. High-resolution digital outcrop modeling further aids geometric mapping, geobody definition, and statistical analysis, though its usefulness for detailed mineralogical and lithological mapping is limited. Hyperspectral imaging offers enhanced spectral resolution for mapping subtle mineralogical differences. In both outcrops and subsurface, differences in carbonate composition can provide key information for distributing porosity and permeability, yet this mapping is highly challenging in field studies due to access difficulties, visible material differences, and sampling resolution. Spectral analysis of limestone–dolomite ratios conducted in laboratory studies indicates theoretical measures for quantitative identification and mapping of dolomite degrees within carbonate rocks. In this study, close-range hyperspectral imaging is applied to outcrops of the Alacón Member, Barranco del Mortero, northeastern Spain, to identify exposed limestone–dolomite geobodies and to quantify the degree of dolomitization across outcrop faces. Hyperspectral imaging is supplemented with photogrammetric outcrop modeling, field spectroscopy, and laboratory sample analysis for empirical validation and uncertainty analysis. Hyperspectral mapping shows that earlier fieldwork utilizing visual inspection of difficult to access outcrop surfaces had overestimated the amount of dolomite in the outcrop. Results indicate that hyperspectral imaging identified dolomite bodies more accurately and reliably than conventional field methods and facilitates the mapping of dolomite contribution in areas modified by dedolomitization, where dolomite content changes by more than ~20%.
BioPrivacy: Development of a Keystroke Dynamics Continuous Authentication System

Session authentication schemes establish the identity of the user only at the beginning of the session, so they are vulnerable to attacks that tamper with communications after the establishment of the authenticated session. Moreover, smartphones themselves are used as authentication means, especially in two-factor authentication schemes, which are often required by several services. Whether the smartphone is in the hands of the legitimate user constitutes a great concern, and correspondingly whether the legitimate user is the one who uses the services. In response to these concerns, Behavioral Biometrics (BB) Continuous Authentication (CA) technologies have been proposed on a large corpus of literature. This paper presents a research on the development and validation of a BBCA system (named BioPrivacy), that is based on the user’s keystroke dynamics, using a Multi-Layer Perceptron (MLP). Also, we introduce a new behavioral biometrics collection tool, and we propose a methodology for the selection of an appropriate set of behavioral biometrics. Our system achieved 97.18% Accuracy, 0.02% Equal Error Rate (EER), 97.2% True Acceptance Rate (TAR) and 0.02% False Acceptance Rate (FAR).
An Implicit Trust-Network construction approach and a Recommendation Methodology for recommender systems

Implicit Trust-Network approach and Recommendation Methodology are employed following the building of a recommender system to improve prediction precision and recommendation quality.Implicit trust-network enhances the prediction exactness of users' preference in the system.The recommendation methodology increases the quality of recommendations.Both methods of implicit trust-network and recommendation methodology are combined to introduce a new robust recommender system called Implicit Trust-Network-based Recommendation Methodology (ITNRM).An open-source ITNRM by MATLAB is prepared to be available with no external dependencies to employ or further extension.
Evaluating Quantitative Metrics of Tone-Mapped Images

Subjective evaluation of tone-mapped images is tedious and time-consuming; therefore, it is desirable to have algorithms for automatic quality assessment. Many full-reference and blind metrics have been developed for this purpose, but their performance is generally evaluated on limited benchmark datasets. This leaves a possibility that the observed performance of the metric could be due to overfitting, and it might indeed not perform well for all scenes. In this work, we propose a novel framework using population-based metaheuristics to evaluate the performance of these metrics without requiring any subjectively evaluated reference dataset. The proposed algorithm does not modify the individual image pixels, instead, the tone-mapping curve is modified to synthesize realistic tone-mapped images for evaluation. Moreover, it is not required to know the underlying model of the evaluated metric, which is treated just like a black box and can be replaced by any other metric seamlessly. Therefore, any new metrics designed in the future can also be easily evaluated by simply replacing just one module in the proposed evaluation framework. We evaluate six existing metrics and synthesize images to which the metrics fail to assign appropriate scores for visual quality. We also propose a method to rank the relative performance of evaluated metrics, through a competition in which each metric tries to find the errors in the scores given by other metrics.
Overcoming National Stereotypes of Undergraduate Students: Implementing Culturally Diverse Materials and Role-Playing in an EFL Classroom

The absence of ethnic stereotypes, together with empathy, reflexivity and critical thinking, is an important skill for engineering students, which contributes to the development of their ability to work in intercultural teams necessary for a successful cooperation in the multicultural working environments all over the world. Stereotypic judgements of undergraduate students about Americans and Tajiks defined the goal of our research – developing a program on overcoming the existing stereotypes of these nationalities and contributing to better understanding of western and eastern cultures. For this purpose, we embedded the material about intercultural communication theories, Anglo-Saxon, Slav and Asian mentality and engaged the students of the experimental group into role-playing everyday, professional and business situations in the English language classes. With the aid of the Bogardus social distance scale questionnaire for measuring the degree of students’ closeness toward diverse ethnic groups and the interview for revealing their attitude toward them in a verbal way, we were able to trace the quantitative and qualitative dynamics of their attitude change. Comparing the results of the pre- and post-test in the control and experimental group, we could identify a shorter social distance of the students enrolled into the developed course toward Americans (from ‘co-workers in the same occupation’ to ‘neighbors on the same street’), and Tajiks (from ‘would exclude from my country’ to ‘as only visitors in my country’). The interview showed that the students of the experimental group could present positive characteristics and a more elaborated description of these nationalities.
Conversational Agent Voting Advice Applications: A Comparison Between a Structured, Semi-structured, and Non-structured Chatbot Design for Communicating with Voters About Political Issues

Voting Advice Applications (VAAs) are online survey tools that inform voters about political parties and their standpoints. Recent research shows that these tools may be improved by integrating a chatbot function that can provide on demand background information about the political issues. This new goal-oriented tool for information-seeking tasks is called a Conversational Agent Voting Advice Application (CAVAA). In an experimental study (N = 185) during the Dutch national elections of 17 March 2021, three CAVAA designs (a structured design with buttons, a non-structured design with a text field, and a semi-structured design with buttons and a text field) have been compared on tool evaluation measures (ease of use, usefulness, and playfulness), and political measures (perceived political knowledge and voting intention). Also, a comparison has been made between users with a high or a low educational level. Results show that the structured design is perceived to be more playful than the non-structured design. Moreover, lower educated users rate the structured and the semi-structured design as easier to use than the non-structured design. Finally, lower educated users also report higher levels of political knowledge after using a CAVAA than higher educated users. This suggests that CAVAAs can be a valuable tool to inform voters.
Real-Time Kafka-Based Topic Modeling and Identification of Tweets

The tremendous growth, popularity, and usage of social media in modern societies has led to the production of an enormous real-time volume of social texts and posts, including Tweets that are being produced by users. These collections of social data can be potentially useful, but the extent of meaningful data in these collections is still of high research and business interest. One of the main elements in several application domains, such as policy making, addresses the scope of identifying and categorizing these texts into natural groups based on the topics to which they refer to, in order to better understand and correlate them. The latter is recently realized through the utilization of Topic Modeling and Identification tasks, for identifying and extracting subjective information and topics from raw texts with the ultimate objective to enhance the categorization of them. This paper introduces an end-to-end pipeline that primarily focuses on the phases of the collection, text preprocessing, as well as utilization of Natural Language Processing and Topic Modeling models, which are considered to be of major importance for the successful Topic Modeling and Identification of Tweets and the final interpretation of them.
Feature Extraction and Sentiment Analysis Using Machine Learning

The role of social networks has bought a tremendous change in the analysis of the opinions. Understanding people sentiments or opinion helps the business or organization to better understand their customers. There are several platforms where people can easily post their views about a service or products, these can be facebook, twitter e.t.c. Feature extraction or aspect extraction becomes important since one needs to know the qualities a product or a service have. In this research, we have analyzed hotel reviews by applying n-gram for feature. As the dataset is always noisy so basic preprocessing steps are applied before extraction. The features extracted are trained and tested by basic machine learning classifiers. Various machine learning algorithms like KNN, SVM, and random forest are used for the analysis of the performance. The evaluation measures are calculated at the end to validate the results. K-fold cross validation scheme is also applied on the dataset to improve the overall accuracy of the results.