Reviewerly/exHarmony huggingface dataset:
Flux qubit-based detector of microwave photons

A theory of detection of microwave photons with a flux qubit-based detector is presented. We consider semiclassical approximation with the electromagnetic field being in a coherent state. Flux qubit is considered as a multilevel quantum system (qudit). By solving the Lindblad equation, we describe the time evolution of occupations of the qudit's levels for readout and reset stages of detection. When considering the reset stage, the time evolution is described by multiple avoided-level crossings, thus providing a multilevel Landau-Zener-Stuckelberg-Majorana (LZSM) problem. In addition to numerical calculations, we present an approximate analytical solution for the description of the reset stage dynamics based on the adiabatic-impulse approximation and rate equation approach. Our theory may be useful for the theoretical description of driven-dissipative dynamics of qudits, including applications such as single-photon detection.
Histopathological Image Classification Using Convolutional Neural Networks for Detection of Metastatic Breast Cancer in Lymph Nodes

Breast cancer is currently one of the most diagnosed oncological diseases worldwide, with thousands of new cases per year. Early detection and identifying its progression are key to overcoming the mortality rate. A recurrent test, to determine how far the disease has spread throughout the patient’s body, is the histological analysis of the sentinel lymph node near the breast. Although an expert pathologist performs this, it is usually an exhausting and time-consuming task, with a high possibility of error. This work presents a method to detect breast cancer metastasis through histological imaging of sentinel lymph nodes using convolutional neural networks. In this study, the performance of three models DenseNet-121, DenseNet-169 and DenseNet-201 are tested and compared. Experimental results indicated that the accuracy, precision, sensitivity and specificity (97.93%, 97.4%, 97.48% and 98.24%) of DenseNet-201 could reduce pathologist errors during the diagnostic process or serve as a second opinion tool.
Diagnosis of Diseases of the Nose Using the Certainty Factor Method

The nose is the human respiratory organ and is a very important sense of smell for patients. Nasal polyps and sinus infections are several diseases that can affect the health of the nose. The nasal cavity reduces inflammation and protects the nasal passages. Many people think these symptoms are flu symptoms and do not immediately seek medical help. So many people find out too late that they have a nasal disease. Public understanding of nasal diseases is still lacking, so many people do not know clearly what disease they suffer from. The certainty factor is to measure something certain and uncertain when making a decision.
Application for Recommending Tourist Attractions on The Island of Java with Content Based Filtering Using Cosine Similarity

Indonesia is a country with a high level of tourism, with natural beauty and historical places and other tourist destinations that continue to develop from year to year. Java Island is one of the islands visited by many tourists with various tourist attractions. Currently, many tourists like to travel, but during holidays tourists are confused about which tourist destination to visit. With advances in internet technology and the abundance of information in online media, it can make it easier for tourists to find information, but because there is so much information provided, it will make tourists confused about deciding and choosing a place. A tourist recommendation application is very necessary to provide good recommendation accuracy to make it easier for tourists to find tourist destinations according to the desired category. To get the best results, the Content Based Filtering method using Cosine Similarity used in this research will provide several tourist recommendations according to the level of similarity of various cities on the island of Java.
Analysis Of Poverty Level Mapping In Riau Province Using The K-Means Method

This research aims to understand the pattern of poverty distribution in Riau Province, identify clusters that reflect similar characteristics and provide a basis for developing more targeted policies. This approach uses machine learning techniques, especially the K-Means algorithm, to form clusters based on poverty level data. The results of the analysis show cluster 0 (C0) with a high poverty level and cluster 1 (C1) with a low poverty level. K-Means proved effective in grouping areas with similar levels of poverty, and provided a strong foundation for further analysis. Evaluation results using the Adjusted Rand Index (ARI), Silhouette Score, Davies-Bouldin Index, and Calinski-Harabasz Index show that the quality of cluster formation is good. This analysis provides detailed insight into poverty patterns in Riau Province and provides an empirical basis for implementing more contextual policies.
Classification of Public Sentiment Toward 2024 Presidential Candidates on Social Media Platform X Using Naïve Bayes Algorithm

This research examines the use of Naïve Bayes algorithm to classify public sentiment on social media X towards Indonesia's 2024 presidential candidates. Against the backdrop of the importance of presidential elections in a democracy, this research focuses on analyzing public sentiment from June to August 2023. The Naïve Bayes method was chosen to process review data about the three main candidates. The classification results provide insight into the positive and negative sentiments of the public, providing benefits for political parties and researchers in understanding public opinion. This research also enhances the understanding of sentiment classification in a political context and provides readers with a useful reference on the Naïve Bayes approach to sentiment classification. In terms of accuracy, the developed naïve bayes model shows a success rate with an accuracy of 74% for Anies Baswedan, 74% for Ganjar, and 88% for Prabowo.
Analysis Sentiment On Social Media Instagram Towards Metaverse Games Saindbox Aplha 2 With Support Vector Machine Algorithm

Metaverse is part from development technology in the metaverse SandBox Alpha 2 Game world taking place worldwide , games in the virtual world like real very possible thing done . metaverse now Already in progress for can be implemented most affected technology to opinion from particular society _ enthusiasts game metaverse saydbox alpha 2. where game can create her world alone and various game For look for missions and coins can make money to sell _ in metaverse sandbox alpha. since emergence exists game that has been appeared on facebook that has been replaced be meta, create attention world public increasingly highlight technology this , someone _ welcome game the with good and some have _ worries to development technology the . So study This will dig analysis sentiment public Indonesia against development and use metaverse technology uses method algorithm Algorithm Support Vector Machine. analysis sentiment that will done on social media Facebook. Programming language used _ is Language Jupyter Notebook Python. Study This get results opinion public Indonesia to metaverse technology that shows behave neutral , negative and positive .
Augmented Reality-Based 3D Technology Learning Media for Human Respiratory Organs

Augmented Reality (AR) technology is selected for seamlessly integrating virtual objects into the real world. Users can represent objects in three dimensions, offering an interactive, real-time experience. The educational sector widely embraces Augmented Reality due to its interactive and timely advantages. This study focuses on the human respiratory system, demonstrating Augmented Reality's efficacy as an interactive learning tool for understanding respiratory organs. The learning material utilizes three-dimensional visualizations with animations, sound, and vibrant colors, enhancing comprehension and preventing boredom. Employing the Multimedia Development Method and software tools like Unity 3D, Blender, CorelDraw, Vuforia, and Visual Studio Code, the research produces a learning media application facilitating the understanding of human respiratory organs. Evaluation through questionnaires, with a 93.16% approval rate from teachers and students, confirms the application's suitability as an effective learning tool.
The Alteration and Mineralization Characteristics of Miocene Porphyry Cu-Au Deposits of Chagai Magmatic Belt, District Chagai, Balochistan, Pakistan

Subduction related Miocene porphyry type deposits are found in the east-west trending Chagai magmaticbelt (CMB) in Pakistan's western margin, Balochistan. This arc exists on the west segment of the Tethyan metallogenicbelt in the south-west of Pakistan. Tethyan metallogenic belt is widely spread over 12,000 km from east to westdirection from Indochina, Tibet, Pakistan, Iran, Turkey and Alpine mountain range in Europe. During the last thirty toforty years several porphyry deposits have been reported in the Chagai magmatic arc, including the very large RekoDiq H14-H15, large Saindak, Tanjeel, H35, H8 and medium Dasht-e-Kain porphyry deposits and many small porphyrycopper deposits. These porphyry deposits were developed within the phase of calc-alkaline type magmatism in theChagai arc. Tonalite, quartz diorite, and monzonite host the porphyry deposits within the adjacent sedimentary wallrock units of Sinjrani Volcanic Group, Juzzak, Saindak, and Amalaf Formations. The concentric zonal pattern ofhydrothermal alteration in these porphyry deposits of the Chagai magmatic arc follows the world's major porphyrydeposits' alteration pattern. Zones of hydrothermal alteration from distal to proximal part includes a potassic alteration,sericitic-clay-chlorite alteration, sericitic alteration, argillic alteration and propylitic alteration. Major ore mineralizationin these deposits is of copper, gold, silver, molybdenum, and minor constituents of other base metals that have beenreported to occur within hydrothermal alteration zones in the Miocene porphyry Chagai magmatic arc.
Regression Trees for Fast and Adaptive Prediction Intervals

Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid prediction regions around point predictions, but its naive application to regression problems yields non-adaptive regions. New conformal scores, often relying upon quantile regressors or conditional density estimators, aim to address this limitation. Although they are useful for creating prediction bands, these scores are detached from the original goal of quantifying the uncertainty around an arbitrary predictive model. This paper presents a new, model-agnostic family of methods to calibrate prediction intervals for regression problems with local coverage guarantees. Our approach is based on pursuing the coarsest partition of the feature space that approximates conditional coverage. We create this partition by training regression trees and Random Forests on conformity scores. Our proposal is versatile, as it applies to various conformity scores and prediction settings and demonstrates superior scalability and performance compared to established baselines in simulated and real-world datasets. We provide a Python package clover that implements our methods using the standard scikit-learn interface.
Quality Does Matter: A Detailed Look at the Quality and Utility of  Web-Mined Parallel Corpora

We conducted a detailed analysis on the quality of web-mined corpora for two low-resource languages (making three language pairs, English-Sinhala, English-Tamil and Sinhala-Tamil). We ranked each corpus according to a similarity measure and carried out an intrinsic and extrinsic evaluation on different portions of this ranked corpus. We show that there are significant quality differences between different portions of web-mined corpora and that the quality varies across languages and datasets. We also show that, for some web-mined datasets, Neural Machine Translation (NMT) models trained with their highest-ranked 25k portion can be on par with human-curated datasets.
LoTa-Bench: Benchmarking Language-oriented Task Planners for Embodied  Agents

Large language models (LLMs) have recently received considerable attention as alternative solutions for task planning. However, comparing the performance of language-oriented task planners becomes difficult, and there exists a dearth of detailed exploration regarding the effects of various factors such as pre-trained model selection and prompt construction. To address this, we propose a benchmark system for automatically quantifying performance of task planning for home-service embodied agents. Task planners are tested on two pairs of datasets and simulators: 1) ALFRED and AI2-THOR, 2) an extension of Watch-And-Help and VirtualHome. Using the proposed benchmark system, we perform extensive experiments with LLMs and prompts, and explore several enhancements of the baseline planner. We expect that the proposed benchmark tool would accelerate the development of language-oriented task planners.
FLASH: Federated Learning Across Simultaneous Heterogeneities

The key premise of federated learning (FL) is to train ML models across a diverse set of data-owners (clients), without exchanging local data. An overarching challenge to this date is client heterogeneity, which may arise not only from variations in data distribution, but also in data quality, as well as compute/communication latency. An integrated view of these diverse and concurrent sources of heterogeneity is critical; for instance, low-latency clients may have poor data quality, and vice versa. In this work, we propose FLASH(Federated Learning Across Simultaneous Heterogeneities), a lightweight and flexible client selection algorithm that outperforms state-of-the-art FL frameworks under extensive sources of heterogeneity, by trading-off the statistical information associated with the client's data quality, data distribution, and latency. FLASH is the first method, to our knowledge, for handling all these heterogeneities in a unified manner. To do so, FLASH models the learning dynamics through contextual multi-armed bandits (CMAB) and dynamically selects the most promising clients. Through extensive experiments, we demonstrate that FLASH achieves substantial and consistent improvements over state-of-the-art baselines -- as much as 10% in absolute accuracy -- thanks to its unified approach. Importantly, FLASH also outperforms federated aggregation methods that are designed to handle highly heterogeneous settings and even enjoys a performance boost when integrated with them.
Depth-aware Volume Attention for Texture-less Stereo Matching

Stereo matching plays a crucial role in 3D perception and scenario understanding. Despite the proliferation of promising methods, addressing texture-less and texture-repetitive conditions remains challenging due to the insufficient availability of rich geometric and semantic information. In this paper, we propose a lightweight volume refinement scheme to tackle the texture deterioration in practical outdoor scenarios. Specifically, we introduce a depth volume supervised by the ground-truth depth map, capturing the relative hierarchy of image texture. Subsequently, the disparity discrepancy volume undergoes hierarchical filtering through the incorporation of depth-aware hierarchy attention and target-aware disparity attention modules. Local fine structure and context are emphasized to mitigate ambiguity and redundancy during volume aggregation. Furthermore, we propose a more rigorous evaluation metric that considers depth-wise relative error, providing comprehensive evaluations for universal stereo matching and depth estimation models. We extensively validate the superiority of our proposed methods on public datasets. Results demonstrate that our model achieves state-of-the-art performance, particularly excelling in scenarios with texture-less images. The code is available at https://github.com/ztsrxh/DVANet.
Learning Interpretable Concepts: Unifying Causal Representation Learning  and Foundation Models

To build intelligent machine learning systems, there are two broad approaches. One approach is to build inherently interpretable models, as endeavored by the growing field of causal representation learning. The other approach is to build highly-performant foundation models and then invest efforts into understanding how they work. In this work, we relate these two approaches and study how to learn human-interpretable concepts from data. Weaving together ideas from both fields, we formally define a notion of concepts and show that they can be provably recovered from diverse data. Experiments on synthetic data and large language models show the utility of our unified approach.
International exchanges and cooperation in art education in Ukraine: challenges and opportunities

The purpose of the article is to study international exchanges and cooperation in art education in Ukraine, to identify challenges and opportunities in the development of these areas in the future. to achieve this goal, the article uses general scientific methods of analysis and synthesis. The methods of abstraction, comparison, content analysis, and the prognostic method were also used, which made it possible to formulate some comments for the future development of international cooperation in art education in Ukraine. The results of the study highlight the importance of international cooperation, which is becoming an increasingly important aspect of modern art education. The analysis of the legislative framework has shown that Ukraine has created favourable conditions for academic exchange, and joining the Bologna system contributes to the internationalisation of higher education in the arts. These trends are also reflected in the development strategies of art universities in different regions of Ukraine, such as Kharkiv, Lviv and Kyiv, where establishing international exchanges is considered an important priority for educational institutions. In addition, the Kyiv National University of Culture and Arts actively uses digital channels to transmit information, especially in remote locations. Although distance learning has its limitations in art education, the conditions of modern reality, such as Russian aggression, create an opportunity to organise international cooperation with foreign partners without physical risk. It is important to note that there are common challenges to the successful implementation of international exchanges and cooperation in art education, such as cultural and language barriers, financial support, visa and administrative restrictions, as well as academic requirements and potential global threats. However, a possible way to overcome these difficulties is to use the international experience of involving NGOs or special state structures that would coordinate the provision of grants for academic mobility of arts students. The conclusions emphasise that it is also worth considering the possibility of special digital platforms that provide opportunities for internships and distance learning, which can be an effective solution for organising international cooperation in the current situation.
Abstraction boundaries and spec driven development in pure mathematics

In this article we discuss how <italic>abstraction boundaries</italic> can help tame complexity in mathematical research with the help of an interactive theorem prover. While many of the ideas we present here have been used implicitly by mathematicians for some time, we argue that the use of an interactive theorem prover introduces additional qualitative benefits in the implementation of these ideas.
Using the Backpropagation Algorithm to Distinguish Arabic Alphabet

In this research, a study of the Arabic alphabet used a multi-layered neural network, which is the backpropagation error. Using the algorithm through the Losing activation function to train the network. The hidden numbers of nodes are 10, the number of cycles is 500, and the error is 0.001, using the Matlab R2013a program. The aim of the study It is the use of the network algorithm to recognize the characters, by training the network to recognize the characters in two cases. The first case is inputting the image of the letter into the grid and the second case is identifying the letter that represents the letter drawn in the image. And it was reached that the algorithm used for the network of nervousness to recognize the Arabic alphabet and then show it correctly.
Sequential composition of propositional logic programs

Abstract This paper introduces and studies the sequential composition and decomposition of propositional logic programs. We show that acyclic programs can be decomposed into single-rule programs and provide a general decomposition result for arbitrary programs. We show that the immediate consequence operator of a program can be represented via composition which allows us to compute its least model without any explicit reference to operators. This bridges the conceptual gap between the syntax and semantics of a propositional logic program in a mathematically satisfactory way.
Language Teaching Materials for Writing Advertising Texts

This study is based on extensive library research, utilizing a wide range of information and resources from various sources. Having the right instructional materials is crucial for the successful completion of any academic program, including Indonesian language classes. Junior high school Indonesian language classes in Class VIII explore a range of subjects, one of which is the art of crafting advertisement copy. To achieve optimal outcomes, it is crucial to utilize appropriate instructional resources while acquiring proficiency in the Indonesian language for advertising purposes. This research aimed to provide a comprehensive description of the different types of advertisements that can be utilized as teaching materials in Indonesian language courses, along with an exploration of the diverse concepts associated with these materials.
Learning program to develop digital skills in high school teachers

The present study focused on evaluating the effectiveness of a learning program aimed at strengthening the digital skills of high school teachers, given the current lack of these skills in the educational environment. A quantitative approach was adopted through a quasi-experimental design, with a population of 73 teachers. A 21-item questionnaire was implemented, validated through expert judgment and the V-Aiken coefficient, with a reliability of 0.826. After the application of the designed program, significant improvements were observed in the experimental group compared to the control group, which shows the positive impact of the program on the development of digital skills. These findings highlight the need to integrate training and continuing education programs to enhance digital skills in the educational field.