Reviewerly/exHarmony huggingface dataset:
EksPy: a new Python framework for developing graphical user interface based PyQt5

This study introduces EksPy Python framework, a novel framework designed for developing graphical user interface (GUI) applications in Python. EksPy framework is built on PyQt5, which is a collection of Python bindings for the Qt libraries, and it provides a user-friendly and intuitive interface. The comparative analysis of EksPy framework with existing frameworks such as Tkinter and PyQt highlights its notable features, including ease of use, rapid development, enhanced performance, effective database management, and the model-view-controller (MVC) concept. The experimental results illustrate that EksPy framework requires less code and enhances code readability, thereby facilitating better understanding and efficient development. Additionally, EksPy framework offers a modern and customizable appearance, surpassing Tkinter’s capabilities. Furthermore, it incorporates a built-in object-relational mapping (ORM) feature to simplify database interactions and adheres to the MVC architectural pattern. In conclusion, EksPy Python framework emerges as a powerful, user-friendly, and efficient framework for GUI application development in Python.
SVD-AE: An asymmetric autoencoder with SVD regularization for multivariate time series anomaly detection

Anomaly detection in multivariate time series is of critical importance in many real-world applications, such as system maintenance and Internet monitoring. In this article, we propose a novel unsupervised framework called SVD-AE to conduct anomaly detection in multivariate time series. The core idea is to fuse the strength of both SVD and autoencoder to fully capture complex normal patterns in multivariate time series. An asymmetric autoencoder architecture is proposed, where two encoders are used to capture features in time and variable domains and a shared decoder is used to generate reconstructions based on latent representations from both domains. A new regularization based on singular value decomposition theory is designed to force each encoder to learn features in the corresponding axis with mathematical support delivered. A specific loss component is further proposed to align Fourier coefficients of inputs and reconstructions. It can preserve details of original inputs, leading to enhanced feature learning capability of the model. Extensive experiments on three real world datasets demonstrate the proposed algorithm can achieve better performance on multivariate time series anomaly detection tasks under highly unbalanced scenarios compared with baseline algorithms.
Spatio-temporal layers based intra-operative stereo depth estimation network via hierarchical prediction and progressive training

Safety of robotic surgery can be enhanced through augmented vision or artificial constraints to the robotl motion, and intra-operative depth estimation is the cornerstone of these applications because it provides precise position information of surgical scenes in 3D space. High-quality depth estimation of endoscopic scenes has been a valuable issue, and the development of deep learning provides more possibility and potential to address this issue. In this paper, a deep learning-based approach is proposed to recover 3D information of intra-operative scenes. To this aim, a fully 3D encoder-decoder network integrating spatio-temporal layers is designed, and it adopts hierarchical prediction and progressive learning to enhance prediction accuracy and shorten training time. Our network gets the depth estimation accuracy of MAE 2.55±1.51 (mm) and RMSE 5.23±1.40 (mm) using 8 surgical videos with a resolution of 1280×1024, which performs better compared with six other state-of-the-art methods that were trained on the same data. Our network can implement a promising depth estimation performance in intra-operative scenes using stereo images, allowing the integration in robot-assisted surgery to enhance safety.
Combining external-latent attention for medical image segmentation

The attention mechanism comes as a new entry point for improving the performance of medical image segmentation. How to reasonably assign weights is a key element of the attention mechanism, and the current popular schemes include the global squeezing and the non-local information interactions using self-attention (SA) operation. However, these approaches over-focus on external features and lack the exploitation of latent features. The global squeezing approach crudely represents the richness of contextual information by the global mean or maximum value, while non-local information interactions focus on the similarity of external features between different regions. Both ignore the fact that the contextual information is presented more in terms of the latent features like the frequency change within the data. To tackle above problems and make proper use of attention mechanisms in medical image segmentation, we propose an external-latent attention collaborative guided image segmentation network, named TransGuider. This network consists of three key components: 1) a latent attention module that uses an improved entropy quantification method to accurately explore and locate the distribution of latent contextual information. 2) an external self-attention module using sparse representation, which can preserve external global contextual information while reducing computational overhead by selecting representative feature description map for SA operation. 3) a multi-attention collaborative module to guide the network to continuously focus on the region of interest, refining the segmentation mask. Our experimental results on several benchmark medical image segmentation datasets show that TransGuider outperforms the state-of-the-art methods, and extensive ablation experiments demonstrate the effectiveness of the proposed components. Our code will be available at https://github.com/chasingone/TransGuider.
Stabilizing machine learning prediction of dynamics: Novel noise-inspired regularization tested with reservoir computing

Recent work has shown that machine learning (ML) models can skillfully forecast the dynamics of unknown chaotic systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (“climate”) can be produced by employing a feedback loop, whereby the model is trained to predict forward only one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this feedback can result in artificially rapid error growth (“instability”). One established mitigating technique is to add noise to the ML model training input. Based on this technique, we formulate a new penalty term in the loss function for ML models with memory of past inputs that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. We refer to this penalty and the resulting regularization as Linearized Multi-Noise Training (LMNT). We systematically examine the effect of LMNT, input noise, and other established regularization techniques in a case study using reservoir computing, a machine learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while the short-term forecasts are substantially more accurate than those trained with other regularization techniques. Finally, we show the deterministic aspect of our LMNT regularization facilitates fast reservoir computer regularization hyperparameter tuning.
CT Image Denoising and Deblurring With Deep Learning: Current Status and Perspectives

This article reviews the deep learning methods for computed tomography image denoising and deblurring separately and simultaneously. Then, we discuss promising directions in this field, such as a combination with large-scale pretrained models and large language models. Currently, deep learning is revolutionizing medical imaging in a data-driven manner. With rapidly evolving learning paradigms, related algorithms and models are making rapid progress toward clinical applications.
A multimodal biometric database and case study for face recognition based deep learning

Recently, multimodal biometric systems have garnered a lot of interest for the identification of human identity. The accessibility of the database is one of the contributing elements that impact biometric recognition systems. In their studies, the majority of researchers concentrate on unimodal databases. There was a need to compile a fresh, realistic multimodal biometric database, nonetheless, because there were so few comparable multimodal biometric databases that were publically accessible. This study introduces the MULBv1 multimodal biometric database, which contains homologous biometric traits. The MULBv1 database includes 20 images of each person's face in various poses, facial emotions, and accessories, 20 images of their right hand from various angles, and 20 images of their right iris from various lighting positions. The database contains real multimodal data from 174 people, and all biometrics were accurately collected using the micro camera of the iPhone 14 Pro Max. A face recognition technique is also suggested as a case study using the gathered facial features. In the case study, the deep convolutional neural network (CNN) was used, and the findings were positive. Through several trials, the accuracy was (97.41%).
Exploring Current Trends in Artificial Intelligence Technology An Extensive Review

Artificial intelligence (AI) has become increasingly pervasive across various domains, including smartphones, social media platforms, search engines, and autonomous vehicles, among others. This study undertakes a scoping review of the current landscape of AI technologies, following the PRISMA framework, with the aim of identifying the most advanced technologies utilized in different domains of AI research. Three reputable journals within the artificial intelligence and machine learning domain, namely the Journal of Artificial Intelligence Research, the Journal of Machine Learning Research, and Machine Learning, were selected for this review. Articles published in 2022 were scrutinized against certain criteria: the technology must be tested against comparable solutions, employ commonly approved or well-justified datasets, and demonstrate improvements over comparable solutions. A crucial aspect of technology development identified in this review is the processing and exploitation of data collected from diverse sources. Given the highly unstructured nature of data, technological solutions should minimize the need for manual intervention by humans. The review indicates that creating labeled datasets is a labor-intensive process, leading to increased research focus on solutions leveraging unsupervised or semi-supervised learning technologies. Efficient updating of learning algorithms and the interpretability of predictions emerge as key considerations in the development of AI technologies. Moreover, in real-world applications, ensuring safety and providing explainable predictions are imperative before widespread adoption can be achieved. Thus, this review underscores the importance of addressing these factors to facilitate the responsible and effective integration of AI technologies into various domains.
Hardware-efficient autonomous error correction with linear couplers in superconducting circuits

Large-scale quantum computers will inevitably need quantum error correction (QEC) to protect information against decoherence. Given that the overhead of such error correction is often formidable, autonomous quantum error correction (AQEC) proposals offer a promising near-term alternative. AQEC schemes work by transforming error states into excitations that can be efficiently removed through engineered dissipation. The recently proposed AQEC scheme by Li et al., called the Star code, can autonomously correct or suppress all single qubit error channels using two transmons as encoders with a tunable coupler and two lossy resonators as a cooling source. The Star code requires only two-photon interactions and can be realized with linear coupling elements, avoiding experimentally challenging higher-order terms needed in many other AQEC proposals, but needs carefully selected parameters to achieve quadratic improvements in logical states' lifetimes. Here, we theoretically and numerically demonstrate the optimal parameter choices in the Star code. We further discuss adapting the Star code to other planar superconducting circuits, which offers a scalable alternative to single qubits for incorporation in larger quantum computers or error correction codes.
Empowering Students Through Digital Literacy: A Case Study of Successful Integration in a Higher Education Curriculum

This study investigates the integration of digital literacy into higher education curricula, focusing on its impact at Online Women University. The introduction delineates the importance of digital literacy in preparing students for success in the digital era. The purpose of the study is to explore the implications of digital literacy integration, encompassing student perceptions, experiences, and technology usage patterns. Methodologically, a cross-sectional research design was employed, with data collected from 150 participants, including students and lecturers, using convenience sampling. Surveys were administered to capture insights into digital literacy, technology usage, and educational experiences. Both descriptive and inferential analyses, including frequencies, percentages, ANOVA, chi-square tests, and regression analysis, were conducted to discern relationships and patterns. Results indicate varying levels of digital literacy integration across faculties, with notable differences in technology usage and perceived empowerment through digital literacy. Notably, the mean score for post-test digital literacy skills increased significantly compared to pre-test scores. The study also reveals high confidence levels in using digital tools for academic purposes among students, influenced by factors such as experience and training. In conclusion, the findings underscore the importance of integrating digital literacy into higher education curricula. They highlight the need for ongoing support and training to enhance digital literacy skills among students and educators. Despite limitations such as convenience sampling, the study contributes valuable insights into the role of digital literacy in academia, emphasizing its significance in preparing students for the digital challenges of the future.
Optical Mark Recognition using Modify Bi-directional Associative Memory

Optical Mark Recognition (OMR) is an important technology for applications that require speedy, high-accuracy processing of a huge volume of hand-filled forms. The aim of this technology is to reduce manual work, human effort, high accuracy in assessment, and minimize time for evaluation answer sheets. This paper proposed OMR by using Modify Bidirectional Associative Memory (MBAM), MBAM has two phases (learning and analysis phases), it will learn on the answer sheets that contain the correct answers by giving its own code that represents the number of correct answers, then detection marks from answer sheets by using analysis phase. This proposal will be able to detect no selection or select more than one choice, in addition, using MBAM make it timeless because it will deal with a complete answer sheet, it no need to extract the answer boxes from the answers sheet. The proposed OMR exhibits accuracy is 99.998% in the recognition of marked and non-marked.
Peningkatan Keterampilan Menulis Puisi Melalui Metode ARCS Dengan Menggunakan Media Gambar

The purpose of this study is to describe the poetry writing ability of grade VIII MTs students of Sorong City. The low ability to write poetry is caused by learning to write poetry that has not been carried out properly so that grade VIII students have difficulty when asked to write poetry.Therefore, the necessary learning strategies and instructional media fun and appropriate. To overcome these problems ARCS Metode and media images of events contained in the newspaper in teaching poetry writing class VIII MTs Sorong Affairs. The research was conducted in two cycles. Each cycle consists of planning, action, observation and reflection. The results of this study indicate the existence of a process of learning, improvement, and change behavior in a positive direction.
Pelatihan Penggunaan Aplikasi Publish Or Perish (POP) Untuk Mencari Referensi Jurnal Pada Mahasiswa Universitas Muhammadiyah Jambi

Every student has the ability to write scientific papers, but in reality there are still many students who have difficulty. This is due to the limited ability of students to access references, as experienced by students at Muhammadiyah University in Jambi. Publish or Perish (PoP) is an application that can help students find references. Therefore, this service activity is carried out with the aim of providing training to students to improve students' abilities in writing scientific papers through Publish or Perish (PoP). The training activity began with the delivery of material on the importance of using Publish of Perish (PoP) in writing scientific papers. Introduction to the Publish or Perish (PoP) application. Delivery of material with lectures, questions and answers. Then carry out practice in the form of downloading the Publish or Perish (PoP) application on the desktop, installing the Publish or Perish (PoP) application. Operate the Publish or Perish (PoP) application. The conclusion from this PKM activity is that the majority of activity participants who are students at Muhammadiyah University of Jambi are able to use the Publish or Perish (PoP) application well.
Analisis Prinsip Kesantunan Dalam Teks Anekdot Pada Buku Cerdas Cergas Berbahasa Dan Bersastra Indonesia Kelas X

Oral and written communication is broad and free. However, the content in it must comply with applicable rules, one of which is the principle of politeness contained in three anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil”. Criticism in it is conveyed by paying attention to the principles of politeness. However, not all speech contained in the text meets the principles of politeness. Therefore, the author was moved to carry out an analysis of the principles of politeness in the anecdotal texts entitled “Liburan Kuli Bangunan”, “Perundungan Tanda Sayang”, and “Korupsi Kecil” contained in the Cerdas Cergas Berbahasa dan Bersastra Indonesia SMA/SMK Kelas X. This analysis aims to discover and examine further the principles of language politeness by the discipline of pragmatics. The approach used in this research is a qualitative descriptive approach. The results of data analysis were collected using reading and note-taking techniques. The results of the analysis in this research found 19 violations of politeness principles, dominated by violations of the thimble agreement. Apart from that, there is compliance with the principles of politeness, totaling 4 utterances, namely 2 on the thimble of agreement, and the other 2 on the thimble of humility and the thimble of feelings. With this research, it is hoped that it will be useful for providing understanding and as additional reference material for further research regarding the principles of language politeness in communication.
Peningkatan Keterampilan Membaca Pemahaman Isi Cerita Anak Melalui Media Handout Buku Bacaan Siswa Kelas V UPT SPF SD Inpres Bontomanai Kota Makassar

The problem in this research is: How do reading comprehension skills improve in concluding the contents of children's stories through developing reading book handouts for class V UPT SPF SD Inpres Bontomanai Makassar City students? The objectives of this research are; to determine the increase in learning outcomes in reading comprehension skills in concluding the content of children's stories through the development of reading book handouts for class V UPT SPF SD Inpres Bontomanai Makassar City students. The type of research used in this research is Classroom Action Research which consists of two cycles where each cycle is carried out in two meetings. The research subjects were teachers and students of class V UPT SPF SD Inpres Bontomanai, Makassar City, totaling 32 students consisting of 17 male students and 15 female students. The use of handout media can improve reading skills and understanding of the contents of children's stories in reading books for class V UPT SPF SD Inpres Bontomanai Makassar City. This can be seen from the results of the analysis of teacher activity observations in cycle I, namely 79.16% and increased in cycle II to 95.83%. Student activity also increased from 78% in cycle I to 95% in cycle II. Meanwhile, the results of the mastery test results in student learning outcomes in reading comprehension skills in cycle I were 37.5%, increasing to 87.5% in cycle II. Thus, it can be concluded that the use of handout media can improve reading skills, understanding the contents of children's stories, reading books for class V UPT SPF SD Inpres Bontomanai, Makassar City.
Relationship of Parenting Styles with Decision-Making and Self-concept Among Adolescents

The current research aimed to examine the relationship of parenting styles with decision-making and self-concept among adolescents (N=400). The sample comprised Multan and Vehari college students and government and private colleges. 200 male and 200 female college students were recruited for the present study. Data were collected through the Parental Authority Questionnaire (Buri, 1989), the Self-Concept Questionnaire (Robson, 1989), and the Melbourne Decision Making Questionnaire (Mann et al., 1997). Results show that self-concept has a significant negative correlation with authoritarian fathers, authoritarian mothers, permissive fathers, and permissive mothers. In contrast, a significant positive correlation is between authoritative fathers and authoritative mothers. Findings revealed that gender moderates the relationship between authoritarian parenting styles and hypervigilance, as well as procrastination. Moreover, gender also moderates the relationship between authoritarian parenting and self-concept. Studies demonstrate that parenting styles, especially authoritarian parenting styles, play a distinctive role in determining adolescents' adaptive decision-making and firm self-concept.