Reviewerly/exHarmony huggingface dataset:
Analisis Penggunaan Bahasa Baku pada Siswa Kelas IV SD Melalui Keterampilan Menulis Karangan Narasi

This study aims to determine the use of standard language in fourth grade students of SD Negeri Giling 03 Pati through narrative essays. This research is a qualitative research by applying descriptive method in the process. Based on the research, it was found that 1) The writing results of fourth grade students at SD Negeri 03 Giling Pati are more than 50% of the number of students who are less in using standard language, 2) Students in using standard words at school, at home, and in everyday life rarely done so that students are not accustomed to writing using the correct standard words according to the rules, 3) When writing students do not use standard words influenced by external and internal factors. Conclutions, external factors that influence students in writing not using standard words consist of: (a) Teacher's mastery of the standard word is lacking, (b) Development of information and communication technology, and (c) Student association and living environment. Internal factors consist of: (a) writing practice using standard words that are still lacking, (b) students' knowledge and understanding of standard words are still lacking, (c) students' interest in writing is low, and (d) students are not careful.&#x0D; &#x0D; Keywords: Standard Language, Writing Skills, Narrative Writing
AU-GAN: Attention U-Net Based on a Built-In Attention for Multi-domain Image-to-Image Translation

Multi-domain image-to-image translation refers to mapping images from a source domain to multiple target domains. The state-of-the-art deep learning models are able to learn mappings among multiple domains without paired image data. The key to most multi-domain image-to-image translation tasks, for instance, object transfiguration, facial attribute editing, etc., is to detect the different regions of interest in the source domain and to convert them from the source domain to different target domains. However, to our knowledge, existing approaches have limited scalability and robustness in detecting the most discriminative semantic part of images in the task of multi-domain image-to-image translation. To address this limitation, we propose Attention U-Net Generative Adversarial Network (AU-GAN), which utilizes three well-known networks (the Attention Gate Network, the Recurrent Residual Convolutional Neural Network (RRCNN) as well as the built-in attention mechanism) for the first time. Experiments indicated that the proposed model not only detects different regions of interest but also achieves figure-ground segregation in the task of image-to-image translation. The proposed models are tested on two benchmark datasets (CelebA, CelebA-HQ). Both qualitative and quantitative results demonstrated that our approach is effective to generate sharper and more accurate images than existing models.
PROVIDING FAIR AND METROLOGICALLY TRACEABLE DATA SETS - A CASE STUDY

In recent years, data science and engineering have faced many challenges concerning the increasing amount of data.In order to ensure findability, accessability, interoperability and reusability (FAIRness) of digital resources, digital objects as a synthesis of data and metadata with persistent and unique identifiers should be used.In this context, the FAIR data principles formulate requirements that research data and, ideally, also industrial data should fulfill to make full use of them, particularly when Machine Learning or other data-driven methods are under consideration.In this contribution, the process of providing scientific data of an industrial testbed in a traceable and FAIR manner is documented as an example.
Quantification of Correlations in Quantum States

Non-classical correlations such as entanglement and quantum discord are essential concepts in the context of quantum technologies nowadays. A plethora of both theoretical and experimental advances on this sort of correlation can be found in the literature. However, it is worth pointing out that new insights on their quantification, detection and application, to name a few, continue to be very welcome. In this chapter, we present a discussion on the quantification of correlations in quantum states. For doing so, we discuss how to quantify the so-called quantum discord in the light of a recently proposed measure inspired by the resource theory for coherence and operationally well-defined in the context of parameter estimation. On the other hand, we also comment on the problem of identifying genuine multipartite correlations in many-body systems. In this case, we discuss a proposal to quantify genuine total (classical plus quantum) correlations at different order $$2\le k\le N$$ .
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm

Many unsupervised anomaly detection algorithms rely on the concept of nearest neighbours to compute the anomaly scores. Such algorithms are popular because there are no assumptions about the data, making them a robust choice for unstructured datasets. However, the number (k) of nearest neighbours, which critically affects the model performance, cannot be tuned in an unsupervised setting. Hence, we propose the new and parameter-free Analytic Isolation and Distance-based Anomaly (AIDA) detection algorithm, that combines the metrics of distance with isolation. Based on AIDA, we also introduce the Tempered Isolation-based eXplanation (TIX) algorithm, which identifies the most relevant features characterizing an outlier, even in large multi-dimensional datasets, improving the overall explainability of the detection mechanism. Both AIDA and TIX are thoroughly tested and compared with state-of-the-art alternatives, proving to be useful additions to the existing set of tools in anomaly detection.
Semantic Parser Using a Sequence-to-Sequence RNN Model to Generate Logical Forms

Neural networks have been shown to replicate neural processing and in some cases intrinsically show features of semantic insight. It all starts with a word; a semantic parser converts words into meaning. Accurate parsing requires lexicons and grammar, two kinds of intelligence that machines are just starting to gain. As the neural networks get better and better, there will be more demand for machines to parse words into meaning through a system like this. The goal of this paper is to introduce the reader to a new method of semantic parsing with the use of vanilla or ordinary recurrent neural networks. This paper briefly discusses how mathematical formulation for recurrent neural networks (RNNs) could be utilized for tackling sparse matrices. Understanding how neural networks work is key to handling some of the most common errors that might come up with semantic parsers. This is because decisions are generated based on data from text inputs. At first, we present a copying method to speed up semantic parsing and then support it with data augmentation.
Combining Algorithmic and Stochastic Data Models

In this paper we combine a random effects model with different machine learning algorithms via an iterative process when predicting commercial real estate asset values. Using both random effects and machine learning allows us to combine the strengths of both approaches. The random effects will be used to estimate a common trend, property type trends, location value, and property random effects for properties that sold more than once. The machine learning algorithm will fit the observed characteristics (features) in a complex non-linear fashion. The model is applied to a small sample of 2,652 transaction in Phoenix (AZ) between 2001â€“2021. We only observe a limited number of property characteristics. The average out-of-sample MAPE is below 11%, which is as good or even better compared to the average appraisal error found in literature. The out-of-sample MAPE is even 9% for properties that sold more than once in the training set. In addition, our model provides indexes and locational heatmaps. These have their own uses and cannot be obtained with standard machine learning algorithms.
Clustering based Indexing of Celebrity Cartoon Images for Retrieval

Celebrity cartoon image has its own attention as it characterizes a famous personality and also serves as a good platform to communicate the scenario, express emotions and makes it easy to perceive without much effort. Retrieval of celebrity cartoon images based on a query cartoon image is a difficult task because of the presence of caricatures, sketches, drawings, painting and artistic style variations. Matching cartoon images and retrieving them is a challenging task as artists may vary the same image with different styles and shape exaggerations for the same feature. A novel clustering based tree is constructed with different levels and each level with varied number of nodes. The nodes are expected to have similar images which are identified using partitional clustering. Tree based indexing is created by designating unique indices for each node. The indices are framed in such a way that they follow their parent node. The given query image traverses the tree till leaf node to retrieve the images based on a distance threshold. If the required number of images are not retrieved, then backtracking is done to retrieve the similar images in the other children nodes of same parent which are reached from the root based on the distance. The IIIT-Cartoon Faces in the Wild (IIIT-CFW) dataset is taken for the experimentation. Images considered in this dataset contains caricatures, paintings, cartoons and sketches of celebrities. Through analysis, it is found that the proposed approach performs well in terms of evaluation measures as precision and recall, time and number of relevant retrieved images. Also it can be used in reduction of search space in recognition of images as the retrieved relevant images could be used as a competent search space. Hence, the proposed approach aids subsequently the celebrity cartoon image recognition. The proposed approach is compared against two models as retrieval using linear search and linear search based on class representative.
Data from An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management

&lt;div&gt;Abstract&lt;p&gt;Pathology Image Informatics Platform (PIIP) is an NCI/NIH sponsored project intended for managing, annotating, sharing, and quantitatively analyzing digital pathology imaging data. It expands on an existing, freely available pathology image viewer, Sedeen. The goal of this project is to develop and embed some commonly used image analysis applications into the Sedeen viewer to create a freely available resource for the digital pathology and cancer research communities. Thus far, new plugins have been developed and incorporated into the platform for out of focus detection, region of interest transformation, and IHC slide analysis. Our biomarker quantification and nuclear segmentation algorithms, written in MATLAB, have also been integrated into the viewer. This article describes the viewing software and the mechanism to extend functionality by plugins, brief descriptions of which are provided as examples, to guide users who want to use this platform. PIIP project materials, including a video describing its usage and applications, and links for the Sedeen Viewer, plug-ins, and user manuals are freely available through the project web page: &lt;a href="http://pathiip.org" target="_blank"&gt;http://pathiip.org&lt;/a&gt;. &lt;i&gt;Cancer Res; 77(21); e83–86. ©2017 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;
Quantitative comparison of IHC staining from An Image Analysis Resource for Cancer Research: PIIP—Pathology Image Informatics Platform for Visualization, Analysis, and Management

&lt;p&gt;Description of a use case to illistrate workflow with Sedeen Viewer&lt;/p&gt;
The application of modern meta-heuristic algorithms for solving complex optimization problems

This paper applies two new meta-heuristic algorithms proposed in 2022 for solving different optimization problems, including the driving training-based algorithm (DTBA) and the average and subtract-based algorithm (ASBA). The considered optimization problems employed in this paper are characterized by different quantities of the dimensions and different involved constraints at various degrees of complexity. The results obtained by the two algorithms are illustrated by three types of convergences, including the minimum, average, and maximum convergences. By analyzing the results obtained by the two applied methods on four different optimization algorithms, DTBA proved itself to be the better applied method over ASBA. Particularly, DTBA has reached the optimal fitness value much faster than ASBA, regardless of how complicated the optimization problem is. From these analyses, DTBA is acknowledged to be the effective algorithm for dealing with the considered optimization problems.
A Survey on Identity-Based Blind Signature

Blind signatures are well-studied building blocks of cryptography, originally designed to enable anonymity in electronic voting and digital banking. Identity-based signature were introduced by Shamir in 1984 and gave an alternative to prominent Public Key Infrastructure. An identity-based blind signature ( $$\textsf{IDBS}$$ ) allows any user to interact directly with the signer without any prior interaction with a trusted authority. The first $$\textsf{IDBS}$$ has been proposed in 2002 and several schemes were proposed since then. Seeking for a full comparison of these primitives, we propose a survey on $$\textsf{IDBS}$$ and list all such primitives that seems to maintain some security. We also classify their security assumptions based on the existing security expectation that have not been formalized yet in the literature. Moreover, we empirically evaluate the complexity of all the operations used in those schemes with modern cryptographic libraries. This allows us to perform a realistic evaluation of their practical complexities. Hence, we can compare all schemes in terms of complexity and signature size.
Dynamic Feature Selection for Structural Image Content Recognition

Structural image content recognition (SICR) aims to transcribe a two-dimensional structural image (e.g., mathematical expression, chemical formula, or music score) into a token sequence. Existing methods are mainly encoder-decoder based and overlook the importance of feature selection and spatial relation extraction in the feature map. In this paper, we propose DEAL (shorted for Dynamic fEAture seLection) for SICR, which contains a dynamic feature selector and a spatial relation extractor as two cornerstone modules. Specifically, we propose a novel loss function and random exploration strategy to dynamically select useful image cells for target sequence generation. Further, we consider the positional and surrounding information of cells in the feature map to extract spatial relations. We conduct extensive experiments to evaluate the performance of DEAL. Experimental results show that DEAL outperforms other state-of-the-arts significantly.
Steel Types Association Analysis in Traffic Construction

Steel and connection joints are important and large amounts of materials for traffic engineering, research on the relationship between the use of different types of steel and connecting joints in the construction project is helpful for reasonable distribution and combination of them. The Apriori algorithm is used to analyze the association. First, according to the data engineering, some with fewer occurrences and unclear definitions are deleted in the original data, secondly, the association algorithm is used to select the frequent itemset and the maximum frequent itemset with high support, and then the association rules of the high index through the confidence level is found out, and finally, the association rules results with high lift in the association rules selected in the previous step are screened.
Psychological readyness as a mechanism of formation of professional maturity of a teacher in conditions of postgraduate education

The formation of the professional maturity of a teacher in the system of postgraduate education is highlighted through the prism of the relevance and modernity of knowledge of synergetics and acmeology, as the methodological basis of the polyparadigmal approach, in general, and competent, in particular. The purpose of the article is theoretical analysis of the integration criteria of professional maturity of a teacher in the content of the competence postulate “The ability to study for a life as a basis for continuous professional training ...”, namely: internal criteria - readiness for continuous personal and professional self-improvement, to professional and personal self-realization, professional competences; external - the effectiveness of professional activities. The study of this problem makes it possible to identify the integration criteria of the professional maturity of the teacher and the theoretical analysis of the basic concepts. The methodological basis of this problem isconsidered; the process of formation of professional maturity of the teacher in the system of post-graduate education through the analysis of reflexive self-regulation, first of all willingness to self-organization, the ability to self-education, to self-realization; criteria specific to the profession of primary school teacher in the institution of general secondary education. In further research and study of the integration components of the professional maturity of a teacher in the system of postgraduate pedagogical education, the priority direction is the study of the conditions for the intensification of the development of pedagogical systems, which make the issue of determining the necessary and sufficient elements of the system of continuous education providing the conditions for the formation of professionalism, acmeological activity and professional maturity of a teacher.
QuantumRNG, A Random Number Generator Using One Qubit

This paper deals with the work done in generating truly random numbers using Quantum Computing. Since Quantum Computing is in its early stage of developing, we have Quantum Computers with only a few Qubits, so efficient use of those Qubits is necessary. We have developed an algorithm that generates nbit truly random numbers using only one Qubit and minimal resources. There exist many algorithms to generate random numbers, but they use more resources compared to our algorithm. In this paper, we are going to discuss about the algorithms to generate single random number and multiple random number. In this work, we have tested our algorithm with few other algorithms and our algorithms outperform other algorithms most of the time. The challenges we faced is, the Quantum Computers now available are prone to noise and they are not giving the proper results as expected, so with the advancement of technology, if the noise is reduced, we might get the expected results with no error.
Intelligent detection of traffic lights using morphological operations

Traffic lights recognition and detection considered an important step in the advanced driver assistance techniques. A smart system designed to identify traffic signals at the main and secondary road junctions at different cities and different weather conditions. Digital image processing techniques used to recognize traffic lights from the recorded video. Segmentation technique based threshold used to distinguish each color of the traffic signal within a threshold for each color. The traffic signal color distinguished using morphological operations. The regionprob function used to calculate the area of traffic light within colored image (cod is available). From these steps, the decision of recognizing the light is ON or OFF is determined effectively and the traffic light is recognized. The results show the efficiency and quality of the proposed system in accurate detection of traffic signals at different times and environment conditions and within different shooting distances.
Target Detection and Tracking Technology of Aerobics Sports Training Based on Hybrid Algorithm

In order to make the aerobics training method more scientific and reasonable, the training features and skills in the video can be extracted through the moving target detection (MTD) and tracking technology (TT). However, in aerobics sports videos, the speed of the moving target is often faster than that of the static target, and the background is more complex, and a single tracking algorithm (TA) cannot play a good role. Therefore, this paper proposes a hybrid tracking algorithm based on the background difference method (BDM) and Kalman filter. The hybrid algorithm models the background of the video image, improves the detection accuracy, and has a higher tracking accuracy than the single algorithm.
Goal Agnostic Learning and Planning without Reward Functions

In this paper we present an algorithm, the Goal Agnostic Planner (GAP), which combines elements of Reinforcement Learning (RL) and Markov Decision Processes (MDPs) into an elegant, effective system for learning to solve sequential problems. The GAP algorithm does not require the design of either an explicit world model or a reward function to drive policy determination, and is capable of operating on both MDP and RL domain problems. The construction of the GAP lends itself to several analytic guarantees such as policy optimality, exponential goal achievement rates, reciprocal learning rates, measurable robustness to error, and explicit convergence conditions for abstracted states. Empirical results confirm these predictions, demonstrate effectiveness over a wide range of domains, and show that the GAP algorithm performance is an order of magnitude faster than standard reinforcement learning and produces plans of equal quality to MDPs, without requiring design of reward functions.
Service quality and institutional reputation on student’s loyalty

This study highlights the effect of service quality and the reputation of private higher institutions in building student loyalty. This research uses a quantitative approach with a survey method. The research respondents were 185 students from PGRI Palembang University. Analysis techniques using variance-based structural equation models (SEM) SmartPLS. The results of the study indicate that the quality of service has a significant direct effect on reputation, not a significant direct effect on student loyalty. Institutional reputation has a very significant direct effect on loyalty. Student service quality has a significant indirect effect on student loyalty through the reputation of the institution, this implies that the reputation of the institution carries an influence relationship (antecedents) between service quality variables on student loyalty. These findings can help higher education institutions to make better strategic plans to increase student loyalty through improving the quality of service and the reputation of the institution.
Photon counting probabilities of the output field for a single-photon input

In this paper, photon-counting statistics for an output field of a single-photon wave packet interacting with a quantum system was derived. The problem was formulated using a collision model with a bidirectional field approximated by two sequences of qubits. It was shown that the collision model formulated for the light with the temporal correlations allows deriving formulae for quantum trajectories in an effective and intuitive way. Starting from the discrete in-time evolution of the composed system, we determined the conditional and unconditional evolution of the quantum system, provided a physical interpretation of quantum trajectories, and determined the analytical formulae for the exclusive probability densities for the reflected and transmitted fields. Finally, our results were applied to a two-level atom, and we also determined analytical expressions for probability densities of arrival times of the successive counts.