Reviewerly/exHarmony huggingface dataset:
Expanding possibilities for quantum state engineering

Abstract Generation of the states with fidelity as close as possible to high-amplitude Schrödinger cat states (SCSs) is studied. The input state in the main mode is exclusively mixed with the Fock states on successive beam splitters after which a number of auxiliary modes emerge. Subsequently, all the modes are properly displaced. The resulting state is projected onto the vacuum states of every auxiliary mode for conditional generation of the target states in the main mode. We demonstrate a possibility to generate Schrödinger cat qudits (SCQs) which may approximate SCSs of large size <?CDATA $\left| \beta \right|$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close="|" open="|"> <mml:mi>β</mml:mi> </mml:mfenced> </mml:math> ranging from <?CDATA $\left| \beta \right| = 2$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close="|" open="|"> <mml:mi>β</mml:mi> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mn>2</mml:mn> </mml:math> to <?CDATA $\left| \beta \right| = 3$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mfenced close="|" open="|"> <mml:mi>β</mml:mi> </mml:mfenced> <mml:mo>=</mml:mo> <mml:mn>3</mml:mn> </mml:math> with high fidelity close to <?CDATA $0.99$?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mn>0.99</mml:mn> </mml:math> . The method is based on the use of the concept of SCQs being the best approximation of the SCSs in the finite Hilbert space with basic displaced number states characterized by the displacement amplitude <?CDATA $\alpha $?> <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mi>α</mml:mi> </mml:math> . Different states (e.g. number state or coherent state or superposed state) are selected as the input to the optical scheme. In particular, we show that an input Schrödinger kitten state (i.e. small-size SCS) can give rise to an output SCS of larger size. We also test the proposed approach in a realistic scenario.
An IIoT-Device for Acquisition and Analysis of High-Frequency Data Processed by Artificial Intelligence

This publication presents the development of an Industrial-Internet-of-Things device. The device is capable of completing several tasks, such as the acquisition of high-frequency measurement data and evaluating data via machine learning methods in an artificial intelligence application. The installed measurement technology generates data which is comparable to data generated by costly laboratory equipment, meaning that it can be used as a low-budget and open-source alternative. A workflow method has been designed that promotes experimental work and simplifies the effort required to implement artificial intelligence solutions. At the end of this paper, the results of the experiment, which aimed to collect measurement data, extract suitable features, and train artificial intelligence models, are presented. Techniques from vibration analysis were used for feature extraction, and concepts for the extrapolation and enhancement of data sets were investigated. The test results have proven that the development is comparable with high-end laboratory equipment. The created application has demonstrated sufficient accuracy in predictions, and the designed process can be used for arbitrary, artificial intelligence-based rapid prototyping.
Development of an Android-Based Mobile Learning Module to Improve the Students Critical Thinking Skills

Development of an Android-Based Mobile Learning Module to Improve Critical Thinking Skills of Junior High School. This study's purpose to specify the validity, practicality, and effectiveness of an android-based mobile learning module on simple ventures and aircraft to repair critical thinking skills for middle school students. The development of an android-based mobile learning module on the material simple ventures and aircraft is based on requirements for exact teaching materials for 21st-century learning which identically leverage technology, information, and communication. The method utilized in this research is ADDIE model development design which consists of five stages, namely analysis, design, development, implementation, and evaluation. The results indicated that (1) the percentage of validity of an Android-based mobile learning module was 91.83% obtained from an average of three validators, (2) the percentage of the practicality of an Android-based mobile learning module was 95.48%, (3) the effectiveness score of an Android-based mobile learning module is 0.63 which is acquired from the N-gain test and 95.91% from students response questionnaire so that it can be inferred that the Android-based mobile learning module is valid, practical and effective to repair critical thinking skill of junior high school students
Indexing Validators

Why would one consider indexing validators? Reuse. The value of reuse seems obvious for structural and semantic &lt;em&gt;specification&lt;/em&gt;, i.e. schemas and controlled vocabularies – there is opportunity to perceive two datasets as aligned.
Business Blockchain Suitability Determinants: Decision-Making through an Intuitionistic Fuzzy Method

Blockchain is one of the innovative and disruptive technologies that has a wide range of applications in multiple industries beyond cryptocurrency. The widespread adoption of blockchain technology in various industries has shown its potential to solve challenging business problems, as well as the possibility to create new business models which can increase a firm’s competitiveness. Due to the novelty of the technology, whereby many companies are still exploring potential use cases, and considering the complexity of blockchain technology, which may require huge changes to a company’s existing systems and processes, it is important for companies to carefully evaluate suitable use cases and determine if blockchain technology is the best solution for their specific needs. This research aims to provide an evaluation framework that determines the important dimensions of blockchain suitability assessment by identifying the key determinants of suitable use cases in a business context. In this paper, a novel approach that utilizes both qualitative (Delphi method) and quantitative (fuzzy set theory) methods has been proposed to objectively account for the uncertainty associated with data collection and the vagueness of subjective judgments. This work started by scanning available literature to identify major suitability dimensions and collected a range of criteria, indicators, and factors that had been previously identified for related purposes. Expert opinions were then gathered using a questionnaire to rank the importance and relevance of these elements to suitability decisions. Subsequently, the data were analyzed and we proceeded to integrate multi-criteria group decision-making (MCGDM) and intuitionistic fuzzy set (IFS) theory. The findings demonstrated a high level of agreement among experts, with the model being extremely sensitive to variances in expert assessments. Furthermore, the results helped to refine and select the most relevant suitability determinants under three important dimensions: functional suitability of the use case, organizational applicability, and ecosystem readiness.
Continuous Variable Systems

Theoretical chapter devoted to the detailed description of continuous variable (CV) systems by consideringthe "phase space," that is spanned by position and momentum for massive particles, quadratures for a quantum electromagnetic field, and phase and charge for electrical circuits. It introduces tools like the Glauber, Husimi, or Dirac phase–space functions, and in more details the Wigner function, that are convenient to describe CV quantum states and their time evolution using the Moyal equation. The chapter gives examples of Wigner functions and their time evolution in the presence of dissipation. It then defines symplectic quantum maps that are simple and important cases of Hamiltonian evolution and are simply related to the covariance matrix containingvariances and correlations. It details the characterization of the quantum processes using the Williamson reduction and Bloch–Messiah decomposition. It discusses Gaussian and non-Gaussian states and the specific measurement procedures for CV states, such as homodyne and double homodyne detection. It introduces the EPR entangled state and, finally, describes how to characterize entanglement and unconditionally teleport Gaussian quantum states.
Indexing Identifier Services

Where do you look for identifiers? If you’re looking for a URI, the IANA has a registry of schemes, like https, mailto, and tel. These days, to resolve an identifier, you generally use the https scheme, which has an authority component in its URI format. You can go with content addressing like IPLD CIDs, but that doesn’t solve where to look – it solves knowing that you found the thing (or that you already have the thing). Authority is hard to persist. So people and...
The Handle System of Persistent Identifiers

The Handle system is a popular choice for the assignment and resolution of globally unique, persistent identifiers. Governance is centralized with the DONA Foundation, and administration is distributed among so-called Credentialed Multi-Primary Administrators (MPAs), of which there are currently nine. You’ve likely heard of at least one MPA: the International DOI Foundation. Each MPA is assigned a number. The DOI Foundation has 10. This is why all DOIs begin with 10.. Each MPA can in...
A Perlisism for Identifiers: Delay Binding

Inference based on semantic retrieval is more robust than inference based on syntactic parsing. identifiers should be as dumb as possible – in other words, should include as little metadata as possible about the thing being identified, leaving all information to be retrieved from metadata repositories rather than inferred from the identifier itself. People always want to infer meaning, and will often try to teach machines to do the same. The problem is that apparent meaning in...
Improving Random Forest with Pre-pruning technique for Binary classification

Random Forest (RF) is a popular machine learning algorithm. It is based on the concept of ensemble learning, which is a process of combining several classifiers to solve a complex problem and improve model performance. The random forest allows extending the notions of decision trees (DT) in order to build more stable models. In this work we propose to further improve the predictions of the trees in the forest by a pre-pruning technique, which aims to optimize the performance of the nodes and to minimize the size of the trees. Two experiments are performed to evaluate the performance of the proposed method; in the first experiment we applied the Classical Random Forest algorithm (CRF) with several different trees. While in the second one, a pre-pruning technique is established on the trees in order to define the optimal size of the forest. Finally, we compared the results obtained. The main objective is to produce accurate decision trees with high precision. The effectiveness of the proposed method is validated on five medical databases; the prediction precision will be improved with 83%, 94%, 95%, 97%, and 81% for Diabetes, Hepatitis, SaHeart, EEG-Eye-State, Prostate-cancer databases respectively. The performance results confirm that the proposed method performs better than the classical random forest algorithm.
THE USE OF SINGING METHOD TO INCREASE STUDENT’S SPEAKING SKILL IN USING PRONOUN AT LANGUAGE CLUB STAI RAKHA AMUNTAI

This research illustrates the increasing of students’ speaking skill through singing method which the formulation of study whether the singing method can increase the student’s speking skill in using pronoun or not. The objectives of this research is to describe the use of singing method to increase student's speaking skill in using pronoun.&#x0D; The techniques of data collecting in this research was Classroom Action Reasearch (CAR) included the observation checklist, test, and documentation. The location of this research was at Language Club STAI RAKHA Amuntai and the subject of the research was 8 students of English class. The instrument of this research included lesson plan, obsevation checklist, and the questions of pre-test and post-test. The result of this research revealed that the pre-test and post-test data is analyze by using statistic formula.&#x0D; According to the report of research result, the use of singing method in using pronoun in the classroom can help students increase their speaking skill. The improvement in the precentage score, which was proved it; the pre-test 1 get 0%, the post-test 1 get 42,8%, the pre-test 2 get 0%, and post-test 2 get 100%. Furthermore according to the observation checklist, the majority of the students response is interesting to the singing method in classroom.&#x0D; Based on the result, it can be concluded that the use of singing method in using pronoun can increase students’ speaking skill as well as their desire to speak. Then, it is suggest to teachers and other researchers, to be able to this research has inspiration to improve students’ speaking skill.&#x0D;
Sentiment Polarity Identification in Banner Headlines of Broadsheets in the Philippines

This study analyses the sentiment polarity of the banner headlines from six broadsheets in the Philippines with the biggest circulation nationwide. The sentiment polarity is the general perception of whether it is worded positively, neutral, or negatively. This study employs five machine learning and artificial intelligence (AI) to conduct the analysis. The results reveal a tone reflecting editorial policy that tends to lean towards the negative tone. While there is a utility for negative framing of the news, this paper argues that a pivot to the positive, particularly in the Philippine setting, is worth considering. Based on current literature, positivity shows the potential to bring in more readers. Publishers can leverage positivity in the news as part of strategies to stem the tide of readership decline. Positivity in the news should start with the headline, through which readers have a first glimpse of the story. Keywords: sentiment polarity identification, sentiment analysis, sentiment polarity of banner headlines
Validating an Identifier Service

How do you validate that an identifier service provides global uniqueness of minted keys, persistence of bindings, and resolution of keys to descriptive metadata? The key problem with testing is that a test (of any kind) that uses one particular set of inputs tells you nothing at all about the behaviour of the system or component when it is given a different set of inputs.1 If you know that a given ID provided by a service is unique, that tells you nothing at all about the uniqueness of...
Tracing Identifiers

At a base level, an identifier is simple to trace – it is the sequence (modulo concurrency) of assertions of which it is a part. In fact, this can be the basis for tracing the representation of a “thing” as the flock of relationships between identifiers, i.e. metadata, that waxes and wanes in association with “the” identifier of the thing.
PENGENALAN MOTIF BATIK PESISIR PULAU JAWA MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK

Coastal Batik is made outside of Solo and Yogyakarta. The use of the term "coastal" is due to the majority of batik production being located in the northern coast of Java, such as Indramayu, Cirebon, Pekalongan, Lasem, and others. Coastal batik is characterized by flexible color selection and patterns, influenced by foreign influences, particularly after the introduction of Islam in the 16th century. The Convolutional Neural Network (CNN) method is commonly used in classifying digital image data. Neurons in CNN are represented in a two-dimensional form, differing in linear function and weight parameters. The CNN extraction process consists of hidden layers, including convolutional, pooling, and ReLU (activation function) layers. The evaluation results of the Convolutional Neural Network model show that it can perform classification and recognize coastal batik images of Java Island, achieving the best results in the first scenario with a training data ratio of 70% and testing data ratio of 30%, resulting in an accuracy of 83%. For future research, it is recommended to increase the number of batik images and capture them directly, while incorporating segmentation or extraction features to measure efficiency and accuracy levels. This will help obtain better results in recognizing the characteristics of coastal batik in Java Island.
A Study on Education Plans Using Big Data Analysis Methods: Focusing on the Feedback of Learning Task Data Using Chat GPT

This study discusses exploring and materializing educational methods that can be used in actual classes with the analysis and visualization of learning task data. In education, big data analysis technology has been used for analyzing the environment outside class, and this study finds a way to utilize this method in an offline classroom. In particular, it was expected that if learners' assignments were processed through topic modeling, sentiment analysis, and network analysis methods and then visualized and given feedback, it would be helpful for interactive classes.&#x0D; In addition, this study focuses on collecting and pre-processing the learner's assignment data so this knowledge can be utilized in a class environment unrelated to computer science. This is based on the spread of big data analysis methods and the development of artificial intelligence technologies such as Chat GPT. Through this, instructors can use computer science technology easily in their class environment.&#x0D; This study presents the analysis and visualization of learning task data as a specific example. If this discussion is used, it will be possible for instructors to process and process learning data in a more automated way, even in educational environments where data analysis is not generalized. This is an example of opening the possibility of convergence between computer science and other disciplines.
An In-depth Interview Study on the Development and Enactment of Korean Language Teachers’ Professionalism in Media Literacy Education

This study conducted in-depth interviews with eight Korean language teachers who actively practiced media literacy education to understand professionalism in media literacy education, which was acquired from and enacted in continuous professional development. The elements of professionalism were divided into beliefs and attitudes, functions, and strategies, and 16 sub-categories were derived.&#x0D; The sub-categories of “Beliefs and Attitudes” included media as a mediator for authentic participation, “responsibility to teach a new language,” the need for education to become a “wise citizen,” everyday exploration and continuous participation in resources for learning, the cycle of challenge and reflection on new instruction, active enjoyment of media, critical thinking about the world, the ability to “match the children’s eye level,” the formation of a supporter identity, and the questioning of identity as Korean language teachers.&#x0D; The sub-categories of “Functions and Strategies” included enacting multifaceted authenticity in learning, integrating and expanding learning experiences, embracing learner diversity, fostering learner autonomy, constructing and utilizing learning communities, and expressing solidarity and seeking consultation with peers and other subject teachers.&#x0D; Educational implications to develop Korean language teachers’ professionalism in media literacy education were discussed.
Background-Oriented Schlieren technique using Fast Checkerboard Demodulation for extending measurement range of pressure of underwater shock waves

We have successfully extended the measurement range by applying Fast Checkerboard Demodulation (FCD) to Background-Oriented Schlieren (BOS) technique, which is a contactless measurement technique. In the experiment, the apparent displacement field on the background image distorted by underwater shock waves were measured using FCD-BOS and contact measurement pressure (hydrophone). The results show that the measurement limitation of FCD is higher than the methods of previous studies which used Optical Flow (OF) and Digital Image Correlation (DIC) algorithms. Since the displacement is detected from the frequency change due to the distortion of the background image, FCD that combines fast Fourier transform (FFT) and a background image with a two-dimensional periodic pattern, can be applied to the large distortion which is difficult to be detected using OF and DIC. The numerical approach in our previous study(Shimazaki et al., 2022) showed that the measurement range of FCD-BOS is strongly influenced by the background pattern. Therefore, this study also performed numerical investigation on the use of synthetic background images with the frequency of the resultant wave of two sine waves of different wavelengths for FCD-BOS. The numerical results show that the measurement range using the synthetic background images with wavelengths of (60,60/12) μm is three times larger than the experimental measurement range using the lattice grid (width: 20 μm) background. In addition to the synthetic background of wavelengths of 60,60/12, the measurement accuracy, phase wrapping, overlap, and aliasing artifacts of those of (40,40/12) and (30,30/12) were investigated. Such results show the superiority of the background with the frequency of sine wave over that of square wave (lattice grid).
Unsupervised Person Re-identification with Multi-Level Feature Contrastive Learning

Unsupervised person re-identification works mainly rely on feature representation learning. In recent years, many methods have used pseudo-labels generated from clustering and applied contrast learning techniques to train models. However, the existing methods only consider intra-class relationships in the mini-batch data, ignoring the relationships between classes, and can not effectively increase the distance between different pedestrian images. In addition, the use of a single feature vector does not fully utilize the information of all instance samples, and cannot represent the class features well. To address this issue, we propose an unsupervised multi-level feature contrastive learning framework for person re-identification. Specifically, the framework combines the contrastive learning loss of the farthest sample from the intra-class relationship, the farthest sample from the inter-class relationship and the cluster centroid contrastive learning loss for the unsupervised person re-identification model, fully mining more discriminative features related to pedestrian identity, and more effectively increasing the separability between classes and the similarity within the class. At the same time, combined with the generated data image as a enhancement dataset to join the network training, increase the diversity of samples, and effectively improve the robustness of the model. The effectiveness of our model is proved on the widely used re-identification dataset.
A Re-ranking Approach for Two-sided Fairness on Recommendation Systems

The filter bubble problem has long constrained users of recommender systems from using it freely. Two stakeholders of the recommendation system, which refer to the content consumer, and the content provider, are disturbed by the meaningless repeating of few high-frequency contents. While most previous work concerns the fairness issue of recommenders from one side, in this paper we provide a new lightweight approach through a re-ranking method increasing fairness for both sides. Experiments on 2 datasets and 4 existing models demonstrate that our proposed algorithm can reduce unfairness and increase overall accuracy. The time complexity for our approach is linear to the total user amount for each user. And it fits all existing recommendation systems that generate a rank score.