Reviewerly/exHarmony huggingface dataset:
Knowledge Integration in Deep Clustering

Constrained clustering that integrates knowledge in the form of constraints in a clustering process has been studied for more than two decades. Popular clustering algorithms such as K-means, spectral clustering and recent deep clustering already have their constrained versions, but they usually lack of expressiveness in the form of constraints. In this paper we consider prior knowledge expressing relations between some data points and their assignments to clusters in propositional logic and we show how a deep clustering framework can be extended to integrate this knowledge. To achieve this, we define an expert loss based on the weighted models of the logical formulas; the weights depend on the soft assignment of points to clusters dynamically computed by the deep learner. This loss is integrated in the deep clustering method. We show how it can be computed efficiently using Weighted Model Counting and decomposition techniques. This method has the advantages of both integrating general knowledge and being independent of the neural architecture. Indeed, we have integrated the expert loss into two well-known deep clustering algorithms (IDEC and SCAN). Experiments have been conducted to compare our systems IDEC-LK and SCAN-LK to state-of-the-art methods for pairwise and triplet constraints in terms of computational cost, clustering quality and constraint satisfaction. We show that IDEC-LK can achieve comparable results with these systems, which are tailored for these specific constraints. To show the flexibility of our approach to learn from high-level domain constraints, we have integrated implication constraints, and a new constraint, called span-limited constraint that limits the number of clusters a set of points can belong to. Some experiments are also performed showing that constraints on some points can be extrapolated to other similar points.
Experimental study on two-octave Indian flute acoustics

The Indian classical flute called ‘Bansuri’ is a side-blown flute originating from the Indian subcontinent. The air blow makes the oscillation of the air column in the flute passage and sounds differently as the exit volume changes. Accordingly, the different notes are configured with an octave and currently, two octaves are in practice. In the direction of exploring the third-octave nodes, the standard two-octave nodes are analyzed in the present study using the analytical correlations and experimental data by frequency analysis performing the Fourier Transformation considering an ‘E’ base flute. Technical parameters affecting the acoustics of flutes such as note frequency, wavelength and sound pressure levels are studied in detail using various diagnostic tools like sound pressure level meter and MATLAB programming tool. The variation in such technical parameters corresponding to each note of two octaves is described in detail with graphical results. It is observed that the frequency of the middle octave notes is nearly two times of the lower octave nodes thereby octave shift forms.
Distill-AER: Fine-Grained Address Entity Recognition from Spoken Dialogue via Knowledge Distillation

Fine-grained address entity recognition from spoken dialogue is an important but challenging task because there are multiple types of address entities distributed across the multi-round dialogue context. Existing work typically formulates this problem as a fine-grained named entity recognition task, which in our scenario suffers from a high cost of training data annotation. On the other hand, large-scale full standard addresses could be easily crawled from the web pages like Google Maps and annotated with fine-grained address tags with limited human effort. Leveraging this, we propose a distillation approach (Distill-AER) for transferring knowledge from the large-scale labeled full standard address dataset to the fine-grained address entity recognition task in a spoken dialogue context scenario. We further construct a labeled spoken dialogue dataset with address entities using the data augmentation paradigm we proposed, which could benefit future research. Experimental results show that Distill-AER significantly outperforms other competitive baselines.
A Session-Based Recommendation Approach with Word Embeddings

Recommender systems have become prominent in past years. Today, many service providers use recommendation systems. Considering the number of products and customers, it is crucially important and necessary to meet the right products to the right customers. Various methods have been applied for recommendation systems. In our study, it is aimed to examine the purchasing relationship between the products and recommend alternative products. To achieve this, product spaces have been created by applying Word2Vec and FastText models, which extract the relationship between words according to the frequency of occurrence of items, on the products in the same basket (Item2Vec). In addition, pre-trained models are applied to product names to measure the similarity and they are ensembled with Item2Vec models with different weights. The results obtained regarding the use of word embedding methods in product recommendation are reassuring and may be applied in following projects.
An Efficient Update Algorithm for Mutable Order-Preserving Encryption

Order-preserving encryption (OPE) produces ciphertexts that preserve the numerical order of plaintexts. Many researchers have focused on designing ideally-secure OPE schemes. This security notion, known as IND-OCPA, has been shown to be achievable by the mutability of ciphertexts and interaction between a database server and a client. This implies that the ciphertexts stored on the server can be updated. Unfortunately, existing update algorithms of mutable OPE schemes are designed to generate ciphertexts uniformly regardless of the distribution of the plaintexts. This leads to inefficiency that requires frequent ciphertext updates for a certain input data pattern (e.g., sequential data). In this paper, we propose a more efficient ciphertext update algorithm that is suitable for mutable OPE schemes. This algorithm makes it possible to reduce the number of updates by considering the input pattern of encrypted data without loss of security. Our experimental results show that, when applied to existing mutable OPE schemes, our update algorithm delivers significantly improved performance on a variety of datasets.
Coregistration of Remote Sensing Image Based on Histogram Kernel Predictability

Registration of remote sensing images has been approached using different strategies; one of the most popular is based on similarity measures. There are different measures of similarity in the literature: Normalized Cross-Correlation (NCC), Mutual Information (MI), etc. Normalized Mutual Information (NMI) has received the most attention in image processing; among the most important limitations are its high computational cost and lack of robustness to strong radiometric changes. For this reason, in this work, we introduce a co-registration approach based on the Histogram Kernel Predictability (HKP). This formulation reduces numerical errors and requires less computing time in comparison to NMI. To the best of our knowledge, this is the first work for registering any remote sensing images by using HKP. Additionally, we propose to use an algorithm based on meta-heuristics called Evolutionary Centers Algorithm (ECA), which allows having fewer iterations to solve the registration problem. In addition, we incorporate a parallelization scheme that permits reducing processing times. The results show that our proposal can solve co-registration problems that the NMI cannot solve while obtaining competitive computational times and registration errors comparable with other existing works in the literature. The HKP approach solves most of all the transformations of a set of simulated registration problems, while the NMI, in some cases, only solves half of the registration problems. Moreover, we compare our approach with feature-based methods in real datasets. This research presents an alternative to remote sensing problems where MI has traditionally been used.
Educational Use of Social Media in Primary and Childhood Education Degrees at a Virtual University

The use of social networks is part of people's daily lives, and university students, in their academic life, make particularly intense use of them on a daily basis for different purposes.This research analyses the nature, intensity, and type of educational use of these social media by childhood and primary pre-teachers who are pursuing their studies online in Spain.To this end, we designed and validated an ad hoc questionnaire, obtaining 268 valid responses to research the specific characteristics of students studying for degrees in early childhood education and primary education and a double degree in early childhood and primary education at a university that is exclusively online.The results show that participants habitually use a variety of social media, but they only use some of them for educational purposes.General social networks offer future teachers versatility, the possibility to develop and leverage social capital, and opportunities to connect formal, nonformal and informal settings.The most used networks were WhatsApp, YouTube and above all Facebook, a reasonably large percentage use Instagram or Pinterest, and, to a lesser extent, Skype, and a lightly under half have Twitter.With regards to how they are used for educational purposes, a majority of respondents reported using them to find classroom activities, new ideas and material that might be of use in their academic work.It was also found that students consumed more information than what they produced, such that social capital is not used in an optimal way.It is concluded that in a university that operates exclusively online, the use of social networks fulfils the function of covering affective and social needs in an optimal way.
Development of Chatbot Supplementary Tool in Science and the Self-Regulated Learning Skills among the Grade 10 Students

This study focused on determining the effectiveness of a developed interactive chatbot as a supplementary tool in studying Science and observing the manifestation of self-regulated learning skills among the Grade 10 students in Mabato National High School. It used a descriptive developmental research design participated by 35 students. Using the Mean and Standard Deviation, results revealed that the features of the evaluated chatbot are highly effective. This implies that the developed interactive Science Chatbot can be used as supplementary material in studying Science in a blended learning modality. Most respondents manifested the description of Self-regulated Learning Skills in terms of goal setting, self-monitoring, self-instruction, and self-reinforcement. This means that the skills were present and visible during the material delivery. The respondents’ Level of Satisfaction with the Use of Interactive Chatbot in terms of usage and content, language used, perception, and difficulties and limitations, most of them agreed that they were satisfied when using the developed interactive chatbot. For treating the difference in the pre-and post-test scores of the respondents in the science competencies test, a t-test was used. The results show a significant difference between the pre-test and post-test. It indicated that the chatbot features affect their test performance because they used it as a reviewer and can return to the topic that appears unclear to them. The result also implies that the chatbot was effective in assisting students in improving their performance on the science competencies test. Keywords: Self-Regulated Learning Skills, Chatbot, Science Competencies, Supplementary Tool
SAR‐IRGF: A novel improved rolling guidance filter for the synthetic aperture radar image

Inspired by the framework of the rolling guidance filter (RGF), a novel improved RGF for synthetic aperture radar (SAR) images is proposed in this letter, named SAR-IRGF. Based on the model of additive noise, the RGF has the complete control of detailed smoothing under a scale measure for optical images. However, the speckle is well known as a multiplicative noise, which invalidates the RGF for SAR images. In this letter, an appropriate edge guidance is utilized to optimize the RGF for SAR image denoising. Compared with the original version, better convergence is achieved. Experimental results on natural Ku-band airborne SAR images show that the proposed SAR-IRGF reduces more speckle while sharpening the edges effectively. Simultaneously, the SAR-IRGF demonstrates a significant nearly 5.5-fold improvement than RGF in the equivalent number of looks (ENL).
Towards Robust Cluster-Based Hyperparameter Optimization

Hyperparameter optimization is a fundamental step in machine learning pipelines since it can influence the predictive performance of the resulting models. However, the setup generally selected by classical hyperparameter optimization based on minimizing an objective function may not be robust to overfitting. This work proposes CHyper, a novel clustering-based approach to hyperparameter selection. CHyper derives a candidate cluster of close or similar hyperparameters with low prediction errors in the validation dataset. Hyperparameters chosen are likely to produce models that generalize the inherent behavior of the data. CHyper was evaluated with two different clustering techniques, namely k-means and spectral clustering, in the context of time series prediction of annual fertilizer consumption. Complementary to minimizing an objective function, cluster-based hyperparameter selection achieved robustness to negative overfitting effects and contributed to lowering a generalization error.
Employment Quality Evaluation Model Based on Hybrid Intelligent Algorithm

In order to solve the defect of large error in current employment quality evaluation, an employment quality evaluation model based on grey correlation degree method and fuzzy C-means (FCM) is proposed.Firstly, it analyzes the related research work of employment quality evaluation, establishes the employment quality evaluation index system, collects the index data, and normalizes the index data; Then, the weight value of employment quality evaluation index is determined by Grey relational analysis method, and some unimportant indexes are removed; Finally, the employment quality evaluation model is established by using fuzzy cluster analysis algorithm, and compared with other employment quality evaluation models.The test results show that the employment quality evaluation accuracy of the design model exceeds 93%, the employment quality evaluation error can meet the requirements of practical application, and the employment quality evaluation effect is much better than the comparison model.The comparison test verifies the superiority of the model.
A Neighborhood Based Particle Swarm Optimization with Sine Co-sine Mutation Operator for Feature Selection

Feature selection is a vital data pre-processing process in many practical applications. Feature selection aims to get rid of those unnecessary features and improve the performance of the classification model. In this paper, a neighborhood based particle swarm optimization with sine cosine mutation operator (NPSOSC) is proposed to select the most informative feature subset. The improvements are included to strengthen its search capacity and avoid local optima stagnation. A distance and fitness based neighborhood search strategy is developed to form stable neighborhood structures for the particles. Each particle adopts superior information from its neighborhoods and the entire swarm can search different regions of the entire search space. The second improvement incorporates a sine cosine mutation operator to enhance the exploration ability and add more randomness into the search process. The improvements will lead to an enhanced balance between exploration and exploitation. To demonstrate the performance of the proposed NPSOSC, seven well-known optimizers are compared with the NPSOSC on 16 well-regarded datasets with different difficulty levels. The experimental results and statistical tests demonstrate the excellent performance of the proposed NPSOSC in exploring the feature space and selecting the most informative features.
Fiber Uncertainty Visualization for Bivariate Data With Parametric and Nonparametric Noise Models

Visualization and analysis of multivariate data and their uncertainty are top research challenges in data visualization. Constructing fiber surfaces is a popular technique for multivariate data visualization that generalizes the idea of level-set visualization for univariate data to multivariate data. In this paper, we present a statistical framework to quantify positional probabilities of fibers extracted from uncertain bivariate fields. Specifically, we extend the state-of-the-art Gaussian models of uncertainty for bivariate data to other parametric distributions (e.g., uniform and Epanechnikov) and more general nonparametric probability distributions (e.g., histograms and kernel density estimation) and derive corresponding spatial probabilities of fibers. In our proposed framework, we leverage Green's theorem for closed-form computation of fiber probabilities when bivariate data are assumed to have <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">independent</i> parametric and nonparametric noise. Additionally, we present a nonparametric approach combined with numerical integration to study the positional probability of fibers when bivariate data are assumed to have <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">correlated</i> noise. For uncertainty analysis, we visualize the derived probability volumes for fibers via volume rendering and extracting level sets based on probability thresholds. We present the utility of our proposed techniques via experiments on synthetic and simulation datasets.
Image Aesthetic Assessment: A Comparative Study of Hand-Crafted &amp; Deep Learning Models

Automatic image aesthetics assessment is a computer vision problem dealing with categorizing images into different aesthetic levels. The categorization is usually done by analyzing an input image and computing some measure of the degree to which the image adheres to the fundamental principles of photography such as balance, rhythm, harmony, contrast, unity, look, feel, tone, and texture. Due to its diverse applications in many areas, automatic image aesthetic assessment has gained significant research attention in recent years. This article presents a comparative study of different automatic image aesthetics assessment techniques from the year 2005 to 2021. A number of conventional hand-crafted as well as modern deep learning-based approaches are reviewed and analyzed for their performance on various publicly available datasets. Additionally, critical aspects of different features and models have also been discussed to analyze their performance and limitations in different situations. The comparative analysis reveals that deep learning based approaches excel hand-crafted based techniques in image aesthetic assessment.
Implementasi Fuzzy Tsukamoto Untuk Menentukan Objek Wisata Terbaik di Kota Sabang Berbasis Web

Sabang City has the characteristics of diverse natural tourism and can be used as a tourist spot for the community. In choosing a tourist spot, it is certainly not easy, apart from distance, cost, transportation and the number of family members, it is also necessary to consider so that the selection of tourist attractions visited by the community is in accordance with their wishes and financial conditions. The purpose of this research is to apply a recommendation system using the fuzzy logic of the Tsukamoto method and a web-based programming language as the language that will be used in making a decision support system application program to determine the best tourist attraction in Aceh. The several stages of work consist of; data collection, analysis, application design and design, implementation, and testing and evaluation. The results of research and testing of the Decision Support System for Determining the Best Tourist Attractions Using the Fuzzy Tsukamoto Method in the City of Sabang Web-Based that have been carried out by the author, several conclusions can be drawn, namely; 1) The expert system application to find the best tourist attraction is an application based on rules to solve problems to determine the best tourist attraction, especially in Sabang City with a high level of accuracy and is used as a tourist reference to determine the tourist attraction to be selected, 2) With the Expert System In this case, tourists or people who need it as a reference about the best tourist objects in Sabang City and as a reference for the Sabang City Government, and 3) The results of the analysis generated from this system are the same as the results of manual calculations using the theory of the Fuzzy Tsukamoto Method so that the accuracy The results are in accordance with the calculations obtained from the trial.
The Panel Spatial Econometric Analysis for Development of Green Intensive Agriculture Based on Edge Computing and Internet of Things

To further promote the modernization of agriculture and the prosperity of green industry, the analyses are made on the intensive level of agriculture by using spatial econometric model under the Internet of Things (IoT), and the optimal defense strategy is adopted for edge network equipment to ensure the security of agricultural information. Initially, the present work introduces the related concepts of agricultural intensive development and analyzes the important role of IoT in the development process of agricultural modernization. Next, it briefly explains the spatial econometric analysis method, introduces two basic spatial analysis models-spatial lag model (SLM) and spatial error model (SEM), and explains their principles in detail. Then, it signifies the characteristics of IoT and edge computing (EC) and designs the optimal defense strategy of edge network equipment from the perspective of IoT. Finally, the simulation experiment is carried out based on the edge network defense strategy, and the spatial econometric analysis is carried out by taking the agricultural intensive development of counties in a Chinese province as an example. The experimental results show that with the increase of the number of edge network devices, the optimal strategy of edge network defense can be adopted while consuming certain computing resources. The agricultural technology input and intensive level in the jurisdiction have high spatial correlation, so it is necessary to establish a spatial econometric model for analysis. Additionally, the statistics of SLM is higher than that of SEM, which shows that SLM can better reflect the technology investment and spatial correlation than SEM does. Both industrial and agricultural division of labor and agricultural production link division of labor can promote the level of intensification, among which the promotion of industrial and agricultural division of labor is not very significant, while the promotion of agricultural production link division of labor is very significant.