Reviewerly/exHarmony huggingface dataset:
EM for Mixture of Linear Regression with Clustered Data

Modern data-driven and distributed learning frameworks deal with diverse massive data generated by clients spread across heterogeneous environments. Indeed, data heterogeneity is a major bottleneck in scaling up many distributed learning paradigms. In many settings however, heterogeneous data may be generated in clusters with shared structures, as is the case in several applications such as federated learning where a common latent variable governs the distribution of all the samples generated by a client. It is therefore natural to ask how the underlying clustered structures in distributed data can be exploited to improve learning schemes. In this paper, we tackle this question in the special case of estimating $d$-dimensional parameters of a two-component mixture of linear regressions problem where each of $m$ nodes generates $n$ samples with a shared latent variable. We employ the well-known Expectation-Maximization (EM) method to estimate the maximum likelihood parameters from $m$ batches of dependent samples each containing $n$ measurements. Discarding the clustered structure in the mixture model, EM is known to require $O(\log(mn/d))$ iterations to reach the statistical accuracy of $O(\sqrt{d/(mn)})$. In contrast, we show that if initialized properly, EM on the structured data requires only $O(1)$ iterations to reach the same statistical accuracy, as long as $m$ grows up as $e^{o(n)}$. Our analysis establishes and combines novel asymptotic optimization and generalization guarantees for population and empirical EM with dependent samples, which may be of independent interest.
Finding Orientations of Supersingular Elliptic Curves and Quaternion Orders

Orientations of supersingular elliptic curves encode the information of an endomorphism of the curve. Computing the full endomorphism ring is a known hard problem, so one might consider how hard it is to find one such orientation. We prove that access to an oracle which tells if an elliptic curve is $\mathfrak{O}$-orientable for a fixed imaginary quadratic order $\mathfrak{O}$ provides non-trivial information towards computing an endomorphism corresponding to the $\mathfrak{O}$-orientation. We provide explicit algorithms and in-depth complexity analysis. We also consider the question in terms of quaternion algebras. We provide algorithms which compute an embedding of a fixed imaginary quadratic order into a maximal order of the quaternion algebra ramified at $p$ and $\infty$. We provide code implementations in Sagemath which is efficient for finding embeddings of imaginary quadratic orders of discriminants up to $O(p)$, even for cryptographically sized $p$.
SPANet: Frequency-balancing Token Mixer using Spectral Pooling Aggregation Modulation

Recent studies show that self-attentions behave like low-pass filters (as opposed to convolutions) and enhancing their high-pass filtering capability improves model performance. Contrary to this idea, we investigate existing convolution-based models with spectral analysis and observe that improving the low-pass filtering in convolution operations also leads to performance improvement. To account for this observation, we hypothesize that utilizing optimal token mixers that capture balanced representations of both high- and low-frequency components can enhance the performance of models. We verify this by decomposing visual features into the frequency domain and combining them in a balanced manner. To handle this, we replace the balancing problem with a mask filtering problem in the frequency domain. Then, we introduce a novel token-mixer named SPAM and leverage it to derive a MetaFormer model termed as SPANet. Experimental results show that the proposed method provides a way to achieve this balance, and the balanced representations of both high- and low-frequency components can improve the performance of models on multiple computer vision tasks. Our code is available at $\href{https://doranlyong.github.io/projects/spanet/}{\text{https://doranlyong.github.io/projects/spanet/}}$.
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models

After the inception of emotion recognition or affective computing, it has increasingly become an active research topic due to its broad applications. Over the past couple of decades, emotion recognition models have gradually migrated from statistically shallow models to neural network-based deep models, which can significantly boost the performance of emotion recognition models and consistently achieve the best results on different benchmarks. Therefore, in recent years, deep models have always been considered the first option for emotion recognition. However, the debut of large language models (LLMs), such as ChatGPT, has remarkably astonished the world due to their emerged capabilities of zero/few-shot learning, in-context learning, chain-of-thought, and others that are never shown in previous deep models. In the present paper, we comprehensively investigate how the LLMs perform in emotion recognition in terms of diverse aspects, including in-context learning, few-short learning, accuracy, generalisation, and explanation. Moreover, we offer some insights and pose other potential challenges, hoping to ignite broader discussions about enhancing emotion recognition in the new era of advanced and generalised large models.
The 19th International Workshop on Termination (WST 2023): Preface, Invited Talk Abstract, and Tool Descriptions

This report contains the proceedings of the 19th International Workshop on Termination (WST 2023), which was held in Obergurgl during August 24--25 as part of Obergurgl Summer on Rewriting (OSR 2023).
Wedge Filter Variation on Wedge Factor in Patients Cancer Breast in Sub Installation Radiotherapy RSUP Prof. Dr. I.G.N.G Ngoerah

The aim of the study was to determine the effect of irradiation time, the percentage of the maximum dose, the distribution of doses at the center point, and the wedge factor on wedge filter variations. The research method uses a wedge filter to measure irradiation time, maximum dose percentage, dose distribution at the center point, and the wedge factor. Wedge value factor without wedge, wedge 15 , wedge 30, wedge 45and wedge 60of 1.000, 1.0059, 1.0102, 1.0137, and 1.0092 respectively. The results of the maximum dose percentage values without wedge, wedge 15 , wedge 30 , wedge 45and wedge 60were 106.76%, 106.80%, 106.79%, 106.85%, and 106.81%, respectively. Conclusion is the value of the wedge factor does not comply with the ICRU standard but the percentage of the maximum dose is in accordance with the International Commisson on Radiation Units (ICRU) standard, there is a wedge effect factor, and irradiation time on the variation of the wedge filter, but there is no effect of the maximum dose percentage and distribution at the center point on the variation of the wedge filter.
SISTEM REKAM MEDIS MENGGUNAKAN PHP dan MySQL BERBASIS WEBSITE

This study aims to develop a website-based medical record system using PHP and MySQL in Arsysoft Indonesia. This system is designed to improve health information management, efficiency of medical services, and interaction between medical personnel, doctors and patients. Literature review involves the concept of medical records, health information systems, and the use of PHP and MySQL in web application development. The research methodology involved research locations at Arsysoft Indonesia and took place from May 2022 to May 2022. Data collection was carried out through interviews and literature studies at Google Scholar, with qualitative data analysis. The result of this research is the development of various interfaces, including login page, admin dashboard, doctor page, patient page, room page, medicine page, medical record page, and report page. Each view is designed to provide functionality according to user needs, optimize information access, and facilitate medical data management. Suggestions proposed include extensive testing, system integration with other components, special attention to data security, user training and cost benefit analysis. It is hoped that the development of this system will support the improvement of the quality of medical services and health information management in Arsysoft Indonesia.
Comparative Analysis of Cryptocurrencies Versus Shares

As the limit between reality and the digital field seems to blur more and more, and this fusion has an impact on several fields of study, such as accounting and audit, the similarity between cryptocurrencies and the shares of companies has to be analysed, as investment items.In the absence of comparisons between the cryptocurrency market and the capital market, investors cannot apply the proper accounting treatment for the activity in which they are involved.Consequently, the aim of this article is setting the existing characteristics and correlations between the cryptocurrency market and the securities market.Besides, the authors consider that this article may contribute to the accounting research, providing useful information to investors and regulatory authorities in accounting.Cryptocurrencies and the prices of shares are somehow correlated after considering the volatility of cryptocurrency.Many of the factors affecting the prices of shares also have an impact on the prices of cryptocurrencies.Investors and traders treat cryptocurrency in the same way in which they treat the shares, so that prices tend to have the same trend.We should be cautious when we invest in cryptocurrencies, as they are still new -the market explores its role as an asset.At the same time, the analysis and presentation of certain statistical data was pursued, like the number of companies listed at the largest stock exchange operators in the world, the stock exchange capitalization, the number of cryptocurrencies at world level, the market capitalization of cryptocurrencies, resulting in a similarity between the two studied elements.
Optimizing the Learning with “Smart Learning” for Special Needs Children in Inclusive School

This community service was carried out at the inclusive school X in Pekanbaru involving all Children with Special Needs. Children with Special Needs or commonly called ABK is a term for children who have physical, mental, social, or emotional conditions that are different from children in general. The use of the "Smart Learning" method which is optimized for children with special needs in inclusive schools is urgently needed to facilitate learning. This service involves collaboration between research teams, inclusive teachers, and principal to design, develop, and test the effectiveness of adaptive learning models that use information and communication technology (ICT). This "Smart Learning" method is specifically adapted to facilitate an inclusive learning process, taking into account the learning needs and preferences of each student. The method is carried out with the stages of observation, implementation and evaluation by presenting pre and post test surveys to find out the effectiveness of learning. The results of the post test show that the application of "Smart Learning" to children with special needs in inclusive schools can increase their active participation, motivate and increase their academic achievement
The impact of credit volume on money supply and economic growth in Azerbaijan: an econometric analysis

Banks are one of the financial entities that perform the work of financial intermediation and, as a result of the loans they provide, help to promote productivity, jobs, and economic development. By increasing their lending volumes, banks will be able to expand their money supply, which will result in a boost to the economy's overall performance. Therefore, substantial research has been carried out to identify the relationship among bank loans as well as economic progress. This study aims to analyze the link among loan volume &amp; money supply in the economy of Azerbaijan. As a method, FMOLS, DOLS and Granger Causality tests, which show co-integration, causality and correlation coefficient from econometric models, were chosen for the data containing quarterly time series of 2006:M1-2021:M9 period. There is a one-way causal connection between money supply and domestic credit volume, so there is a one-way causal correlation among economic growth and domestic credit volume resulting from the findings. It was also discovered that there is a long-term link between the quantity of money and credit accessible to citizens in their nation and the rate at which their economy expands. On the basis of FMOLS test, a 1 percent increase in domestic loan volume results in a 1.078183 percent increase in the money supply. (1.022554 according to DOLS). Performing the FMOLS test does not affect economic development. The FMOLS test found that for every one percent increase in domestic loan volume, economic growth improves by 0.44243 percentage points, based on the number of domestic loans (0.439953 percent according to DOLS).
Portable multiscale form measurement technique for structured specular surfaces based on phase measuring deflectometry

Abstract Propelled by the rapidly growing demand for function incorporation and performance improvement, various specular components with complex structured surfaces are broadly applied in numerous optical engineering arenas. Form accuracy of the structured surfaces directly impacts the functioning of the specular components. Because the scales of these structures and/or the importance of their functions are usually designed differently, the structures require different measurement demands in scale, lateral resolution, and accuracy. In this paper, a multiscale form measurement technique is proposed based on triple-sensor phase measuring deflectometry for measuring structured specular surfaces. The proposed technique contains two sub-phase measuring deflectometry(PMD)-systems. Each sub-system works as a single segmentation PMD (SPMD) system and is designed to have different measurement scales, lateral resolutions, and accuracies to meet the measurement demands of the targeted surfaces. Two imaging sensors in the proposed technique cover the measured full-scale surface. The specular surface is separated into several continuous segments through algorithms and the spatial relationship of the continuous segments is established based on absolute depth data calculated through the triangular relationship between the two imaging sensors. The third imaging sensor with a long working distance only captures the field of the small-scale structures and reconstructs the structures based on gradient data to improve the structures’ reconstruction resolution and accuracy. In order to make it suitable for portable and embedded measurement, a compact configuration is explored to reduce system volume. Data fusion techniques are also studied to combine the measurement data of the two sub-systems. Experimental results demonstrate the validity of a portable prototype developed based on the proposed technique by measuring a concave mirror with small-scale structures.
A genetic algorithm inspired method for semantic web service discovery using entropy-based deep neural network clustering and singular value decomposition

Abtract: Web Services play a crucial role in the realm of e-business applications in view of the rapid growth of web servce technologies. Effectively discovering the most pertinent web services from an extensive collection is pivotal for seamless application execution. However, extracting the most pertinent web services is still a challenging task due to poor query results with lack of semantics, low precision and recall rates. While past studies have employed various methods for web service discovery, this paper incorporates a collaborative filtering approach by employing singular value decomposition (SVD) matrix factorization to capture the principal sematics hidden behind a user query and the description in services. The proposed work aims to enhance the retrieval of relevant outcomes for user queries by leveraging a genetic algorithm inspired collaborative SVD (G-CSVD) matrix factorization technique. This is complemented by an entropy-based deep neural network clustering process, culminating in efficient service discovery. Post-clustering, the knowledge base is integrated into user queries, utilizing the SPARQL endpoint for data retrieval and manipulation within the resource description framework (RDF) database. As a result, the proposed methodology yields pertinent outcomes from the RDF data store in response to user queries.This research contributes an in-depth analysis encompassing execution time, service discovery time, cluster accuracy, precision, recall, F1-score, search time, execution time, root mean square error (RMSE), and mean square error (MSE). Furthermore, a comparative assessment against conventional methods highlights the efficiency and effectiveness of the proposed approach.
Online Profiling Elements of Prosody in Speech Communication (O-PEPS-C)

The Online Profiling Elements of Prosody in Speech Communication (O-PEPS-C) is an online web-based adaptation of the Profiling Elements of Prosody in Speech Communication clinical test (PEPS-C, Peppé et al. 2003) that assesses the comprehension and production of different aspects of prosody. Like the PEPS-C, the O-PEPS-C has 14 subtests, each one of which contains 8 pairs of items that differ minimally. For example, in the lexical stress subtests, the 16 items include both the noun ˈIMport and the verb imPORTˈ. Over the course of a subtest, each member of a pair is the target answer once. The PEPS- C and O-PEPS-C assess different aspects of prosody in the same order (first prosodic form, then declarative/question prosody, affective prosody, lexical stress, phrase stress, phrase boundary, and contrastive stress), and in both versions of the test, comprehension subtests always precede production subtests. The 14 subtests and their items are described in this work.
ANALISIS KEMAMPUAN MEMBACA CEPAT DAN PEMAHAMAN PADA SISWA KELAS IV SD NEGERI KARANG TENGAH 12 KOTA TANGERANG

This study aims to determine the speed reading skills and comprehension of fourth grade students at Karang Tengah 12 Public Elementary School, Tangerang City. This research uses a qualitative approach with descriptive methods. Data collection techniques used are tests, observations, interviews and documentation. The subjects of this study were fourth grade students at Karang Tengah 12 Public Elementary School, Tangerang City. The results of this study indicate that almost all students' speed reading skills and comprehension are good, but there are some students whose reading skills are still lacking, namely in reading fluency, reading intonation, sentence use, punctuality, confidence, not being able to read text correctly and clearly, answering questions related to the text, explaining the meaning contained in the reading, interpreting difficult words in the reading text and transcribe information in reading.
PERBANDINGAN KNN DAN SVM UNTUK KLASIFIKASI KUALITAS UDARA DI JAKARTA

The growth and economic development of a city is one of the factors causing air pollution because air quality has been mixed with various components of chemical compounds such as motor vehicle exhaust gases and factory smoke waste. Data mining is a method to find out information about air pollution in the city of Jakarta. The data mining method used is classification because this method can process air pollution standard index (AQI) parameter data into information that can show the level of air quality per day using the K-Nearest Neighbor algorithm and Support Vector Machine. &#x0D; The result of the application of data mining for air quality classification in Jakarta is that the Support Vector Machine algorithm has better accuracy performance compared to the K-Nearest Neighbor algorithm. The Support Vector Machine algorithm uses the RBF kernel and 100 kernel parameter gets an accuracy value of 98%, precission of 97%, recall of 97%, and F1-Score of 97% while the K-Nearest Neighbor algorithm uses the number of K as much as 6 gets an accuracy value of 96%, precission of 96%, recall of 93%, and F1-Score of 94%.
Rancangan Sistem Informasi Perpustakaan Berbasis Web Pada SMK Negeri 1 Tanjungpinang

Technology that is now growing rapidly is widely used by the community for various activities, such as administrative activities. Of the many administrative activities that exist, the library is one of them that requires information technology in data management. The library is a building of an institution or agency that has the task of providing, compiling, and managing various collections of books. SMK Negeri 1 Tanjungpinang is one of the schools that still processes library data manually, which still does not utilize existing information technology. Data collection that is still done manually will cause problems, where data collection is slow and inefficient. Therefore, the design of this library information system will assist the administrative activities of SMK Negeri 1 Tanjungpinang by having a library data management website. This is useful for librarians in facilitating all data management processes, including making membership cards, the process of making reports, making and collecting data on borrowing and returning books. Making a web-based library information system at SMK Negeri 1 Tanjungpinang using the laravel framework with mysql database.
PREDIKSI CURAH HUJAN DI KABUPATEN BADUNG, BALI MENGGUNAKAN METODE LONG SHORT-TERM MEMORY

Rainfall is the height of rainwater that falls on a flat area, assuming it doesn't evaporate, doesn't seep, and doesn't flow. Rain levels are measured in mm (millimeters). The target of the research being conducted is in Badung Regency, Bali because Bali is a tourist area that is often visited by tourists and from Indonesian itself, so predictions of meteorology, such as rainfall will greatly impact tourism. In this test, predictions use the Long Short Term Memory (LSTM) method, using daily weather data from the BMKG from 2010 to 2020 as training data and daily weather data for 2021 as prediction data. Based on the test results above, the results show that the two LSTM tests with LSTM Model 128.64 and LSTM Model 64.32 have low MAE and MAPE error values. From First Scenario, the Mean Absolute Error (MAE) value is 8.97246598930908 and Mean Absolute Percentage Error (MAPE) value is 1.7657206683278308%. From Second Scenario, the Mean Absolute Error is 9.706669940783014 and Mean Absolute Percentage Error is 1.9028466692362323%. From the MAE and MAPE values obtained in these two scenarios, it can be proven that from the evaluation results of Rainfall predictions in Badung Regency, Bali, the predictions can be said to be very accurate because they have an error value of less than 10.
Implementation of Visual Mind Mapping Strategy for Improving Students’ Performance

The aim of the article was to substantiate the implementation of the visual mind mapping (VMM) strategy for improving the effectiveness of students’ learning in the theoretical and experimental aspect. The focus was the use of VMM to organize learning of students of non-Humanities majors when studying the subjects of the Humanities curriculum, which are mandatory for the undergraduate level. Methods: the following methods were used to collect empirical data and interpret research results: online monitoring through questionnaires in Google Forms; testing with the involvement of the computer programme potential using the EdApp microlearning platform tools; Computer-Assisted Personal Interviewing (CAPI), classic methods of conducting scientific research and statistical data processing. The prospects of further research include a wider integration of VMM with the involvement of special software and determining its impact on the effectiveness of the students’ education.