Reviewerly/exHarmony huggingface dataset:
Data Mining and Big Data in Social Media Public Opinion Monitoring

With the popularization of the Internet, social media has become one of the most common communication methods, and a large amount of information is generated on social media at every moment. These pieces of information contain a large amount of public opinion data, making social media an important source of public opinion. In the application of social media public opinion monitoring, utilizing Big Data (BD) technology to process and mine massive data from social media has become an important means of public opinion monitoring. At present, there are mature technologies and tools in this field, but further research is needed for multilingual and multicultural Data Mining (DM), as well as BD analysis and linkage prediction of micro and macro public opinion. This article uses DM and BD analysis to monitor public opinion on social media, and verifies it by conducting keyword mining on corresponding web pages in the medical, transportation, education, public security, and military industries. It can be concluded that the number of keywords captured by DM and BD analysis methods in healthcare, transportation, education, public security, and military is 980, 830, 789, 445, and 657, respectively. The number of keywords captured by traditional methods is 670, 545, 489, 245, and 557. From this, it can be seen that through DM and BD analysis techniques, valuable public opinion information can be extracted from social media data, achieving the goal of real-time tracking of social public opinion and predicting the development of micro and macro public opinion.
An Architecture for More Fine-Grained Hidden Representation in Named Entity Recognition for Biomedical Texts

This paper introduces a model for Biomedical Named Entity Recognition (BioNER). Different from existing models that mainly rely on pre-trained models, i.e., PubMedBERT, the proposed model is empowered by using PubMedBERT as the main backbone for mapping input sequences to contextual vectors. To learn more fine-grained hidden representation and effectively adapt to the recognition downstream task, the model stacks BiLSTM and CRFs on top of PubMedBERT. Given an input sentence, the model first maps the sentence into contextual vectors by PubMedBERT. The vectors are next fed into a BiLSTM layer for learning a more fine-grained hidden representation that serves as the input for sequence labeling by using CRFs. We confirm the efficiency of the model on benchmark corpora. Experimental results on 29 diverse datasets indicate that the proposed model obtains promising results compared to good as well as state-of-the-art baselines. The ablation study also shows the behavior of the model in several aspects.
Data-Driven Narratives: Unleashing the Potential of R for Journalistic Storytelling

The digital age has seen a significant rise in data journalism and the importance of data-driven narratives. However, the creation of these narratives is often constrained by insufficient data models and the need for efficient tools to facilitate the process. This paper explores the potential of R, a programming language and free software environment for statistical computing and graphics, in enhancing journalistic storytelling. R offers a wide range of packages that can be particularly useful for journalists, including ggplot2 for data visualization, dplyr for data manipulation, tidytext for text analysis, and sf for spatial data analysis. These packages facilitate data cleaning, analysis, and visualization, crucial for data-driven storytelling. However, using R in journalistic contexts is challenging, underscoring the need for further research and development. This paper concludes that R holds significant potential for enhancing journalistic storytelling, but more work needs to be done to realize this potential and overcome the existing challenges fully.
Evading the Schawlow-Townes limit in feedback oscillators

We develop a general quantum theory of feedback oscillators and use it to study the origin of quantum noise in such systems, the limitations posed by quantum noise, and systematic techniques to evade these limitations. For a laser, the quantum noise limit is the Schawlow-Townes limit, which can be evaded using squeezing or entanglement.
Enhanced quantitative birefringence imaging supported by unsupervised topological analysis of polarized light microscopy

We introduce quantitative birefringence spatial measurements coupled with unsupervised topological analysis of polarization microscopy to reconstruct crystal grain axis orientations in composite uniaxial materials. Proof-of-principle demonstration is given using thin-section geological samples.
SSIM-Edge-CNN-U2Net: Two-Stage Residual Learning to Restore Natural Scene Images Through Multimode Fiber

Proposed SSIM-Edge-CNN-U 2 Net model improves loss function and adopts two-stage residual learning, achieving higher quality of restored natural scene images from speckles through multimode fiber.
Fully Integrated Photonic Tensor Core Accelerator for Neural Network Applications

Machine Learning applications have exploded in recent times. Here, we present the first fully-integrated Photonic Tensor Core accelerator, capable of computing Neural Networks by integrating all the optical components, from laser to photodetectors.
Spatial Mode N00N-States and the Quantum Gouy Phase

Photonic N00N states can be realized in spatial modes of light in a single beam. Here, their enhanced phase sensitivity translates to improved angular sensing and enables the observation of the quantum Gouy phase.
Spectrally-Pure Visible-Telecom Photon Pairs via SiN Waveguide

We propose SiN waveguide-based intrinsically spectrally-pure visible-telecom photon pair source with biphoton spectral purity of ∼92%. Such compact, on-chip sources could have applications in various quantum technologies such as sensing, simulation, computing, and communication.
A Framework for Constructing an Image Sensor Based Random Number Generators

This work proposes a framework for determining and setting up a random number generators based on spontaneous emission and photon absorption using commercial off-the-shelf complementary metal-oxide-semiconductor image sensors and Light Emitting Diodes.
First demonstration of an 8-node Mbps quantum access network based on passive optical distribution network facilities

Quantum access network with 8 nodes and a secret key rate of over 6 Mbps per user is firstly demonstrated using passive optical distribution network facilities, paving the way of high-performance and scalable quantum-secured network.
Development and Application of an Integrated Laser-Enabled Silicon Photonic Tensor Core

This study introduces a Silicon Photonic Tensor Core, exemplifying advanced integration and harnessing optical computing’s speed and energy efficiency. Demonstrating image convolution with less than 7% error, the system showcases substantial potential for optical computing applications.
Hybrid Ensemble Broad Learning System for Network Intrusion Detection

In the current era of big data, securing computer networks and preventing cyberattacks have become a major challenge. In this regard, we present a novel approach for addressing the imbalanced problem of network traffic data. Specifically, the proposed method MC-OCBLS, leverages the maximum correntropy criterion (MCC) to develop a one-class broad learning system. The robustness of the one-class classification model is enhanced by maximizing the correlation entropy. Moreover, considering the limited generalization capability of individual models, we propose the boosting ensemble model SMC-OCBLS, which assigns higher weights to misclassified samples and classifiers with lower error rates. To increase the variety of classifiers in the ensemble framework, feature columns are randomly disrupted during training to construct different feature spaces. Training multiple hybrid classifiers using various feature spaces further enhances the accuracy and generalizability of the model. The experimental results demonstrate the superiority of the proposed model over other advanced approaches.
Quantum advantage in noise sensing of additive-noise channels

Classical strategies for sensing the noise level of an additive-noise channel cannot provide energy-enhanced precision. Two-mode squeezed vacuum probes enable energy-enhanced precision and approach a universal lower bound on it.
Rancang Bangun Aplikasi Input Data Analisa Tandan Untuk Pemilihan Pohon Induk di PT. Palma Inti Lestari (PT. PIL)

The palm plantation is a major commodity in the Riau province where PT. Palma Inti Lestari (PT. PIL) take a role as a production unit of palm oil sprouts. Sprouts produced by PT. PIL have gone through the research and testing phase, first. The best quality sprouts come from the best quality parent tree. To get the parent tree with the best quality, then researched the palm oil fruit from different palm trees in one month. In determining the best quality parent tree, determined by lab officials through a series of data analysis. The data is oil content data, thin shell, big fruit, And that's a lot of credit. It replicates the data from this research as well (thousand). Right now, the research records are still manual, where data from the field is input to the computer using Microsoft Excel. Given the amount of research data that should be inserted one by one into the excel table, with a storage system done only by stand alone computer, causing a riot/probably an error in input data. As one solution resolves error in input data with resource state in the field, then an application is created to facilitate employees as operators in the input data of cluster analysis that will later be selected to make parent trees in producing quality sprouts. This data input application is designed using UML and built using VBA that supports Microsoft Access Applications
Art Education in the Life of Оleksandr Кoshyts

Attention is drawn to the place of art education in the life of the outstanding conductor and composer Oleksandr Koshyts. Periodization of the artist’s pedagogical activity before emigration (in Ukraine) and abroad (in the USA and Canada). The features and values that were characteristic of Oleksandr Koshyts as a teacher are highlighted: patriotic dominant, strength of will and ability to master someone else’s will, deep concentration, great knowledge in the fields of philosophy, theology, literature, history, archeology, folklore, colossal musical training, knowledge of foreign languages (including English), good knowledge of psychology, which helped him become a great connoisseur of the human soul and its emotional manifestations, audience, love and respect for students, consonant work to improve their professional level, which contributed to its formation as a teacher-innovator. The components of his charismatic personality are established: 1) psychological and physical personalities of qualities, traits, abilities and possibilities; 2) the image of the leader; 3) communicative aspects of influence (speaking skills, interaction with people, methods of influencing them, public speaking, charismatic leader as a destroyer of customs and creator of rituals; 4) motivation of the leader (pragmatic motives, motives of metaphysical, missionary level, mission).
Pemanfaatan Teknologi Adsorbsi Sebagai Solusi Penyediaan Air Bersih Di Kabupaten Tanjung Jabung Barat Provinsi Jambi

&#x0D; &#x0D; &#x0D; &#x0D; Jambi Province is a province that has the 3rd largest peatland area in the island of Sumatra. The area of ​​peat land in Jambi Province reaches 736,227.20 ha or about 14% of the area of ​​Jambi Province. The area of ​​peat in West Tanjung Jabung Regency is 154,598 ha. Water on peatlands is not a good water category because water on peatlands often contains seeds or certain substances, which are not clear, which can cause diseases that support human survival. Peat water is one of the groundwater that does not meet health requirements that is used by some people who live in tidal or coastal areas. Due to this condition, it does not support the community to get clean water which causes the community to use peat water. Adsorption technology is one of the solutions to provide clean water in the village. The adsorbent used is activated carbon from cassava peels. It is hoped that this technology can help the surrounding community to help with the problem of lack of clean water.&#x0D; &#x0D; &#x0D; &#x0D;
Workshop Pembuatan Video Pembelajaran Untuk Pengoptimalan Media E-Learning

The pandemic period requires a change in behavior habits. Not in the learning process in SMK. Interactive learning media will facilitate the delivery of material by the teacher and easily accessible to most students compared to the delivery of text or PowerPoint. To support e-learning content that is already running at SMK Negeri 1 Jenangan, teachers need to be provided with the provision of making learning videos as media to support learning. The training in making video-based learning media is expected to increase teacher competence in independent learning in student learning to learn virtually during the pandemic season. This learning making training video uses the Camtasia application which is easy for all teachers to practice. So the results can add to the content of e-learning that is already used by the school. From the workshops that have been carried out, as many as 20 teachers of SMK Negeri 1 Jenangan Ponorogo who attended the Workshop were able to use Camtasia to make Learning Videos.
Pengembangan Modul Keuangan Berbasis Android pada Aplikasi Point of Sales

In this community service activity, a financial module was added to the point of sales application that had been previously implemented at MSME Marikh Salatiga. This is because the issue of financial flows has not been accommodated which requires several different accounts. In addition, it is necessary to arrange an online food commission. The development of this module is expected to accommodate financial flows in Marikh Salatiga so as to avoid mismatches in the nominal balance of the entire account and can be monitored in real time. From community service activity with Marikh Salatiga partners, it can be concluded that the financial module implemented in the point of sales application helps Marikh Salatiga make it easier to monitor balance positions in various payment method accounts and can provide more valid financial flow information
Credit Card Fraud Detection Using Machine Learning Algorithms

Abstract: The study delves into the prevalent issue of credit card fraud within electronic transactions. Employing machine learning techniques, including logistic regression, the research focuses on the development and evaluation of fraud detection models. The dataset undergoes comprehensive pre-processing steps, addressing common issues such as missing data and class imbalance. Various machine learning algorithms are explored, with a specific emphasis on logistic regression. Sampling techniques are employed to balance the dataset, ensuring equal representation of legitimate and fraudulent transactions. Model evaluation involves metrics like accuracy, precision, recall, and F1 score to assess performance. The outcomes shed light on the logistic regression model's effectiveness in detecting fraudulent transactions. The study also outlines inherent limitations, acknowledging challenges such as imbalanced datasets and the dynamic nature of fraud tactics. In summary, this research contributes to the ongoing advancements in credit card fraud detection, utilizing machine learning for enhanced security in electronic transactions. The discussed findings and limitations serve as a valuable foundation for future developments in this critical field.
Role of library automation in school libraries

The study contracts about the importance of Automation in school libraries in the digital age. It is beneficial to students, librarians, faculty members, administrators and parents for several reasons. The library is the resource centre that supports and boosts the curriculum for the school. The study basically covers the objectives, application software, automation vocabulary, advantages of applications etc. Finally, the study covers the automation function of school libraries in the digital era.
MASKED AND UNMASKED FACE RECOGNITION ON UNCONSTRAINED FACIAL IMAGES USING HAND-CRAFTED METHODS

In this study, the face recognition task is applied on masked and unmasked faces using hand-crafted methods. Due to COVID-19 and masks, facial identification from unconstrained images became a hot topic. To avoid COVID-19, most people use masks outside. In many cases, typical facial recognition technology is useless. The majority of contemporary advanced face recognition methods are based on deep learning, which primarily relies on a huge number of training examples, however, masked face recognition may be investigated using hand-crafted approaches at a lower computing cost than using deep learning systems. A low-cost system is intended to be constructed for recognizing masked faces and compares its performance to that of face recognition systems that do not use masks. The proposed method fuses hand-crafted methods using feature-level fusion strategy. This study compares the performance of masked and unmasked face recognition systems. The experiments are undertaken on two publicly accessible datasets for masked face recognition: Masked Labeled Faces in the Wild (MLFW) and Cross-Age Labeled Faces in the Wild (CALFW). The best accuracy is achieved as 94.8% on MLFW dataset. The rest of the results on different train and test sets from CALFW and MLFW datasets are encouraging compared to the state-of-the-art models.
In situ analysis of plastic ﬂow near interfaces and free surfaces

Abstract Spatio-temporal analysis of large strain plastic ﬂow at or near interfaces and free surfaces is important for understanding practical problems in the cutting and sliding of metals. In this context, the use of direct in situ imaging, coupled with digital image correlation (DIC), has gained popularity in the past couple of decades since it does not require a priori assumptions about the nature of the deformation ﬁeld or the interface. While DIC is a well established technique, its application to dynamically evolving free surfaces and interfaces is quite challenging. The techniques used, such as hierarchical grid reﬁnement or post analysis interpolation are either spatially restricted or can lead to signiﬁcant data loss. In this work, we present an alternative experimental method that circumvents both these limitations by resorting to ensemble averaging of deformation ﬁelds over a number of related, yet independent, random grids. The resulting ﬁelds are accurate to second order and are benchmarked against standard 1D and 2D test cases, before being applied to two situations arising in deformation processing—frictional sliding and orthogonal machining. The result is the ability to accurately estimate residual surface strains on the cut/processed material surface without exploiting a priori information about the deformation.&amp;#xD;
Continuous-time quantum harmonic oscillator state engineering

Abstract The center of mass motion of trapped ions and neutral atoms is suitable for approximation by a time-dependent driven quantum harmonic oscillator whose frequency and driving strength may be controlled with high precision. We show the time evolution for these systems with continuous differentiable time-dependent parameters in terms of the three basic operations provided by its underlying symmetry, rotation, displacement, and squeezing, using a Lie algebraic approach. Our factorization of the dynamics allows for the intuitive construction of protocols for state engineering, for example, creating and removing displacement and squeezing, as well as their combinations, optimizing squeezing, or more complex protocols that work for slow and fast rates of change in the oscillator parameters.
Research on News Text Clustering for International Chinese Education

The automatic clustering technology for news texts is increasingly mature, enabling the monitoring and analysis of the development of international Chinese education. In this study, a large-scale corpus of news texts of international Chinese education was collected. Seven classification models were used, including logistic regression, decision trees, random forests and naive Bayes, to achieve automatic classification of news texts with an accuracy rate of 85%. Additionally, three principles for data collection and five categories for news classification were established. From the classification features of news texts, this study revealed the current development status of international Chinese education in multiple countries worldwide. Through the analysis of news from different countries, distinct characteristics were identified in terms of the content, format, and reporting style of Chinese education. These findings enable the dynamic monitoring and analysis of the development trends of international Chinese education in various countries around the world.
ENHANCING LETTER RECOGNITION THROUGH PLAYING PLASTICIN IN GROUP A PAUD TERPADU QATHRUN NADA BANJARMASIN

The background of this research is the low cognitive development of children in recognizing letters caused by the less attractive learning strategies and media used. The solution used to improve children's ability to recognize letters is through playing with plasticine. The aim of this research is to improve learning to increase children's ability to recognize letters. This research used the Classroom Action Research (PTK) method which was carried out in 2 cycles with research subjects of 9 children in group A of Integrated PAUD Qathrun Nada Banjarmasin. Based on the results of research conducted, playing with plasticine can increase teacher activity by up to 97%, children's activity reaches 100%, and children's ability to recognize letters reaches 100%. Based on these results, it can be concluded that playing plasticine can improve children's ability to recognize letters in group A of Integrated PAUD Qathrun Nada Banjarmasin.
IMPROVE THE ABILITY TO RECOGNIZE NUMBERS THROUGH DICE GAMES IN GROUP A PAUD TERPADU QATHRUN NADA BANJARMASIN

The background of this research was the low cognitive abilities of children in recognizing numbers. This problem was caused by a lack of varied media and methods, the use of media tools and learning resources, as well as less engaging teaching strategies by teachers/educators. This study aimed to describe the teacher’s activities, children’s activities, and analyze the learning outcomes of children’s cognitive abilities in recognizing numbers through dice games in Group A of Integrated Early Childhood Education Qathrun Nada Banjarmasin. This research used Classroom Action Research (CAR) conducted in two cycles with 6 male and 6 female subjects in Group A of Integrated Early Childhood Education Qathrun Nada Banjarmasin. Data were obtained through observation, documentation, and data analysis. Based on the activities conducted, teacher activities increased from the “Good” category to the “Very Good” category, children’s activities increased from a score of 63% to a score of 100%, and children’s learning outcomes increased from the “Sufficient” category to the “Very Good” category. The research concluded that teacher activities, children’s activities, and children’s ability to recognize numbers increased from Cycle I to Cycle II. The results of this study could be used as a reference for selecting models in introducing numbers in the cognitive aspects of children