Reviewerly/exHarmony huggingface dataset:
Multicultural Values-Based English Teaching Materials in Higher Education

The condition of a pluralistic society will potentially be prone to social conflict, thus disrupting the integrity of harmonious social relations in society. The research aims to develop English language teaching materials based on multicultural values in higher education. This research uses research and development involving Analysis, Design, Development, and implementation (ADDIE) models and mixed methodology. In collecting data, several instruments were used: observation, in-depth interviews, Focus Group Discussions (FGD), document analysis, and questionnaires. This research involved student participants and lecturers. The data analysis technique used is qualitative and quantitative descriptive analysis. The findings of this research show that in English textbooks, readings and twenty exercises that contain multicultural values are expected to be found. In the English for Agribusiness textbook, researchers did not find multicultural values. In addition, the results of the needs analysis show that teachers and students need to provide appropriate techniques and strategies for teaching English through multicultural values. The validity results show that multicultural education can be taught using English material. Based on the effectiveness results, it shows that multicultural education is effective. Multicultural education needs to be involved by including multicultural values in English teaching materials, especially in universities. For future research, it is necessary to find English language teaching materials with other methodologies and specific fields at the higher education level.
LIKARI (Five Words in A Day) Application to Improve Vocabulary Mastery in Japanese Language Learning

Students experience several obstacles in understanding and mastering Japanese vocabulary. Students need to have appropriate and exciting strategies for learning vocabulary. Therefore, this research aims to develop interactive learning media using an Android-based application called "Likari." This application has a specific goal of using a method of mastering five Japanese vocabulary words in a day. This research uses Research and Development (R&amp;D) research using the Borg &amp; Gall development model. Data collection methods use observation, questionnaires, and tests. Field trials were conducted on a small scale on 34 class XI students. The experts involved in this research were 2 material experts and 2 media experts. The techniques used to analyze data are qualitative descriptive analysis, quantitative, and inferential statistics. Research shows that the "Likari" application is suitable or valid for school learning. The research results show that media experts assessed the implementation at 92%, and material experts rated it at 87%. The application is also included in the convenient category with a score percentage of 92%. Using the "Likari" application effectively improves students' mastery skills in learning Japanese, where the N gain score is 73.76%.
Reading Strategies on the Ability to Read Narrative Texts and Explanatory Texts

In general, our students' reading ability is still low. The aim of this research is to analyze the effect of learning reading strategies on the ability to read narrative texts and explanatory texts in fourth grade elementary school students. This research is a quasi-experimental research (quasi experiment). The research design used was a pretest-posttest control-group design. The population of this study was 111 students, while the sample in this study was 76 students. The independent variable in this research is learning reading strategies, while the dependent variable is students' ability to read narrative texts and students' ability to read explanatory texts. The data collection method used is the test method. The collected data on students' ability to read narrative text and ability to read explanatory text were analyzed using the MANOVA statistical analysis technique. Based on the research that has been conducted, it was found that first, there is a significant influence of learning reading strategies on the ability to read narrative texts of fourth grade elementary school students. Second, there is a significant influence of reading strategy learning on the ability to read explanatory texts of fourth grade elementary school students. Third, Simultaneously, there is a significant influence of reading strategy learning on the ability to read narrative texts and the ability to read explanatory texts in fourth grade elementary school students.
Semantic embedding for quantum algorithms

The study of classical algorithms is supported by an immense understructure, founded in logic, type, and category theory, that allows an algorithmist to reason about the sequential manipulation of data irrespective of a computation’s realizing dynamics. As quantum computing matures, a similar need has developed for an assurance of the correctness of high-level quantum algorithmic reasoning. Parallel to this need, many quantum algorithms have been unified and improved using quantum signal processing (QSP) and quantum singular value transformation (QSVT), which characterize the ability, by alternating circuit ansätze, to transform the singular values of sub-blocks of unitary matrices by polynomial functions. However, while the algebraic manipulation of polynomials is simple (e.g., compositions and products), the QSP/QSVT circuits realizing analogous manipulations of their embedded polynomials are non-obvious. This work constructs and characterizes the runtime and expressivity of QSP/QSVT protocols where circuit manipulation maps naturally to the algebraic manipulation of functional transforms (termed semantic embedding). In this way, QSP/QSVT can be treated and combined modularly, purely in terms of the functional transforms they embed, with key guarantees on the computability and modularity of the realizing circuits. We also identify existing quantum algorithms whose use of semantic embedding is implicit, spanning from distributed search to proofs of soundness in quantum cryptography. The methods used, based in category theory, establish a theory of semantically embeddable quantum algorithms, and provide a new role for QSP/QSVT in reducing sophisticated algorithmic problems to simpler algebraic ones.
APPLICATION OF MULTISTAGE CLUSTERING FOR MAPPING ECONOMIC POTENTIAL IN EAST JAVA PROVINCE

This study aims to map the economic potential in East Java Province based on GRDP according to business field category. Multistage clustering is a method developed for outlier data and datasets with large variance. Multistage clustering is a combination of Ordering Points to Identify the Clustering Structure (OPTICS) and K-Means. The first stage was grouped using OPTICS. The outlier data resulting from the clustering stage is used as a dataset in the second stage using K-Means. The performance of this method is compared with several other methods, namely: K-Means, DBSCAN â€“ K-Means, Agglomerative, Fuzzy C-Means (FCM), Possibilistic C-Means (PCM), and Fuzzy Possibilistic C-Means (FPCM) based on the characteristics of the Silhouette score and Davies-Bouldin score. Multistage clustering was chosen as the best method with a Silhouette score of 0.442 and Davies-Bouldin score of 0.388. With the Elbow method and the two metrics, the optimum number of clusters is 8 clusters. The results of this mapping method, the City of Surabaya forms a separate cluster which has the highest economic potential in 15 categories of business fields. Next Gresik, Pasuruan, Sidoarjo, and Probolinggo have the second highest economic potential with 10 categories of business fields ranking in the top 3.
A Study on the Data UX Design Training Method for Understanding the Growth Management Framework

Since the Fourth Industrial Revolution, when creating services and products and earning revenue, there has been an emphasis on not just focusing on sales, but on the rationale for the revenue and efforts to attract customers in an attractive way. To address this, the ability to quantitatively and qualitatively analyze data throughout the service process has been emphasized, and companies that collect data want their employees to test hypotheses and draw conclusions based on data. As a result, data is applied in a variety of jobs, but there are difficulties in learning data for non-professionals who have never worked with data. In this study, we interviewed junior-level incumbents who are working with data to find out when they started learning data and what kind of experiences they had during the learning process, so that non-experts can learn data-driven UX design more easily, and educators who provide data-driven learning services can provide education that meets the needs of users.
KOreaPAS: TAPAS based Korean-Specific Table Question Answering Model

Table Question Answering (QA) aims to answer questions based on semi-structured tables. Unlike text data, tables possess a unique two-dimensional structure, driving the exploration of specialized learning approaches to enhance language models’ understanding of tables. However, while Table QA research is advancing rapidly in English, its development in Korean is still in its early stages. To mitigate this gap, we present KOreaPAS, specifically designed for Korean Table QA tasks. KOreaPAS is based on TAPAS’s architecture, and its learning process consists of two stages: pre-training and fine-tuning. In the publicly available Korean tabular dataset for pre-training language models, approximately 36.5% instances lack text information related to tables, and it can potentially hinder the models’ learning of various correlations between text and the table during pre-training. To address the issue, we introduce a table-text mapping method that retrieves the most relevant text for the table from Wikipedia pages. Further, we propose a multi-granularity fine-tuning strategy that utilizes the three granularities of the table structure for both training and inference. Experimental results robustly confirm the effectiveness of the proposed approaches in enhancing the comprehension abilities of language models towards questions over tables. Specifically, KOreaPAS demonstrated the highest performance among currently published benchmark models in tests conducted on two Korean Table QA datasets, thus establishing a new standard in Korean Table QA tasks.
Impact of possible errors in natural language processing-derived data on downstream epidemiologic analysis

To assess the impact of potential errors in natural language processing (NLP) on the results of epidemiologic studies.We utilized data from three outcomes research studies where the primary predictor variable was generated using NLP. For each of these studies, Monte Carlo simulations were applied to generate datasets simulating potential errors in NLP-derived variables. We subsequently fit the original regression models to these partially simulated datasets and compared the distribution of coefficient estimates to the original study results.Among the four models evaluated, the mean change in the point estimate of the relationship between the predictor variable and the outcome ranged from -21.9% to 4.12%. In three of the four models, significance of this relationship was not eliminated in a single of the 500 simulations, and in one model it was eliminated in 12% of simulations. Mean changes in the estimates for confounder variables ranged from 0.27% to 2.27% and significance of the relationship was eliminated between 0% and 9.25% of the time. No variables underwent a shift in the direction of its interpretation.Impact of simulated NLP errors on the results of epidemiologic studies was modest, with only small changes in effect estimates and no changes in the interpretation of the findings (direction and significance of association with the outcome) for either the NLP-generated variables or other variables in the models.NLP errors are unlikely to affect the results of studies that use NLP as the source of data.
Identity and Digital Communication: Concepts, Theories, Practices. Rob Cover

Journal Article Identity and Digital Communication: Concepts, Theories, Practices. Rob Cover Get access Identity and Digital Communication: Concepts, Theories, Practices. Rob Cover. New York: Routledge, 2023, 192 pp. ISBN(HB): 9781032283968; ISBN(PB): 9781032283951; ISBN(EB): 9781003296652. US$34.86 (P/B). Xuqing Li, Xuqing Li Lanzhou University of Technology, China E-mail: lixuqing6688@163.com Search for other works by this author on: Oxford Academic Google Scholar Yuxin Ren Yuxin Ren Lanzhou University of Technology, China Search for other works by this author on: Oxford Academic Google Scholar Digital Scholarship in the Humanities, fqad094, https://doi.org/10.1093/llc/fqad094 Published: 27 December 2023
THE TRANSFORMATION OF DON JUAN. ESSAYS ON THE DRAMATURGY OF ALEXANDER DARGOMYZHSKY’S OPERA THE STONE GUEST. DEDICATED TO THE 210TH ANNIVERSARY OF THE COMPOSER

The problem of semantic content and musical dramaturgy of an experimental and absolutely brilliant phenomenon of Russian musical theatre - the opera The Stone Guest by A.Dargomyzhsky, are addressed in the study. Namely, an attempt was made to study Dargomyzhsky’s recitative opera following the primary source, the little tragedy of the same name by A.Pushkin, in terms of the authors’ spiritual worldview. In this case, spirituality is understood not only as the richness of the inner world but also as the faith-centric creativity characteristic of representatives of Russian culture of the 19th century. The research perspective reveals a lot of new information in a seemingly familiar work.
Disentangled representation transformer network for 3D face reconstruction and robust dense alignment

Abstract This paper proposes a disentangled representation transformer network (DRTN) for 3D dense face alignment and reconstruction. Unlike traditional 3DMM-based approaches, the target parameters, such as shape, expression, and pose, are individually estimated without considering their direct influences on one another and then jointly optimized. Hence, DRTN aims to enhance the representation of facial attributes in a semantic sense by learning the correlation of different 3D facial attribute parameters. To achieve this, we present a novel strategy to design disentangled 3D face attribute representation, which decomposes the given facial attributes into identity, expression, and poses. Specifically, the 3D face parameter estimation in the regression network depends on the correlation of other face attribute parameters rather than being independent. The branching of the identity component aims to reinforce learning the expression and pose attributes by preserving the overall face geometry structure and identity. Accordingly, the expression and pose parts of the branch preserve the consistency of expression and pose attributes, respectively. Moreover, DRTN helps refine the reconstruction and alignment of facial details in large poses, mainly by coupling other facial attribute parameters. Extensive qualitative and quantitative experimental results on widely evaluated benchmarking datasets demonstrate that our approach achieves competitive performance compared to state-of-the-art methods.
Context-aware Optimization for Bandwidth-Efficient Image Analytics Offloading

Convolutional Neural Networks (CNN) have given rise to numerous visual analytics applications at the edge of the Internet. The image is typically captured by cameras and then live-streamed to edge servers for analytics due to the prohibitive cost of running CNN on computation-constrained end devices. A critical component to ensure low-latency and accurate visual analytics offloading over low bandwidth networks is image compression which minimizes the amount of visual data to offload and maximizes the decoding quality of salient pixels for analytics. Despite the wide adoption, JPEG standards and traditional image compression techniques do not address the accuracy of analytics tasks, leading to ineffective compression for visual analytics offloading. Although recent machine-centric image compression techniques leverage sophisticated neural network models or hardware architecture to support the accuracy-bandwidth trade-off, they introduce excessive latency in the visual analytics offloading pipeline. This paper presents CICO, a Context-aware Image Compression Optimization framework to achieve low-bandwidth and low-latency visual analytics offloading. CICO contextualizes image compression for offloading by employing easily-computable low-level image features to understand the importance of different image regions for a visual analytics task. Accordingly, CICO can optimize the trade-off between compression size and analytics accuracy. Extensive real-world experiments demonstrate that CICO reduces the bandwidth consumption of existing compression methods by up to 40% under comparable analytics accuracy. Regarding the low-latency support, CICO achieves up to a 2x speedup over state-of-the-art compression techniques.
Drug Cost Optimization Using Evidence-Based Decision Support Systems

The article presents an overview of specialized medical information systems that perform ABC/VEN analysis. It provides a comparison of specific examples, describes their advantages and disadvantages, as well as the possibility of automating the processes of searching for information on the proven effectiveness of drugs, selecting alternative drugs within one ATC group, selecting alternative drugs within the framework of the current clinical recommendations of the Ministry of Health of the Russian Federation, assessing drug-drug interactions, as well as the reference information from the instructions for use of the drugs.
Visualizing Local Superconductivity of NbTiN Nanowires to Probe Inhomogeneity in Single-Photon Detectors

NbTiN has a high critical temperature (Tc) of up to 17 K, making it a great candidate for superconducting nanowire single-photon detectors (SNSPDs) and other applications requiring a bias current close to the depairing current. However, superconducting inhomogeneities are often observed in superconducting thin films, and superconducting inhomogeneities can influence the vortex nucleation barrier and furthermore affect the critical current Ic of a superconducting wire. Superconducting inhomogeneities can also result in stochastic variations in the critical current between identical devices, and therefore, it is crucial to have a detailed understanding of inhomogeneities in SNSPDs in order to improve device efficiency. In this study, we utilized scanning tunneling microscopy/spectroscopy (STM/STS) to investigate the inhomogeneity of superconducting properties in meandered NbTiN nanowires, which are commonly used in SNSPDs. Our findings show that variations in the superconducting gap are strongly correlated with the film thickness. By using time-dependent Ginzburg–Landau simulations and statistical modeling, we explored the implications of the reduction in the critical current and its sample-to-sample variations. Our study suggests that the thickness of NbTiN plays a critical role in achieving homogeneity in superconducting properties.
Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving

Abstract: Fisheye cameras, valued for their wide field of view, play a crucial role in perceiving the surrounding environment of vehicles. However, there is a lack of specific research addressing the processing of significant distortion features in segmenting fish-eye images. Additionally, fish-eye images for autonomous driving face the challenge of few datasets, potentially causing over fitting and hindering the model's generalization ability.&#x0D; Based on the semantic segmentation task, a method for transforming normal images into fish-eye images is proposed, which expands the fish-eye image dataset. By employing the Transformer network and the Across Feature Map Attention, the segmentation performance is further improved, achieving a 55.6% mIOU on Woodscape. Additionally, leveraging the concept of knowledge distillation, the network ensures a strong generalization based on dual-domain learning without compromising performance on Woodscape (54% mIOU).
CrowdGraph: Weakly-Supervised Crowd Counting via Pure Graph Neural Network

Most existing weakly-supervised crowd counting methods utilize Convolutional Neural Networks (CNN) or Transformer to estimate the total number of individuals in an image. However, both CNN-based (grid-to-count paradigm) and Transformer-based (sequence-to-count paradigm) methods take images as inputs in a regular form. This approach treats all pixels equally but cannot address the uneven distribution problem within human crowds. This challenge would lead to a decline in the counting performance of the model. Compared with grid and sequence, the graph structure could better explore the relationship among features. In this paper, we propose a new graph-based crowd counting method named CrowdGraph, which reinterprets the weakly-supervised crowd counting problem from a graph-to-count perspective. In the proposed CrowdGraph, each image is constructed as a graph, and a graph-based network is designed to extract features at the graph level. CrowdGraph comprises three main components: a dynamic graph convolutional backbone, a multi-scale dilated graph convolution module, and a regression head. To the best of our knowledge, CrowdGraph is the first method that is completely formulated based on the Graph Neural Network (GNN) for the crowd counting task. Extensive experiments demonstrate that the proposed CrowdGraph outperforms pure CNN-based and pure Transformer-based weakly-supervised methods comprehensively, and achieves highly competitive counting performance.
Summary of the 1st Interpretability and Robustness in Neural Software Engineering (InteNSE 2023)

InteNSE is an interdisciplinary workshop for research at the intersection of Machine Learning (ML) and Software Engineering (SE) and would be a pioneer in emphasizing the implicit properties of neural software engineering and analysis. Due to recent computational advancements, ML has become an inseparable part of the SE research community. ML can indeed improve and revolutionize many SE tasks. However, most research in the AI and SE communities consider ML as a closed box, i.e., only considering the final performance of the developed models as an evaluation metric. Ignoring the implicit properties of neural models, such as interpretability and robustness, one cannot validate the model's actual performance, generalizability, and whether it is learning what it is supposed to do. Specifically, in the domain of SE, where the result of ML4SE tools is code synthesis, bug finding, or repair, interpretability and robustness are crucial to ensure the reliability of the products.