Reviewerly/exHarmony huggingface dataset:
Combining external-latent attention for medical image segmentation

Authors:
raw_author_name: Enmin Song
raw_affiliation_string: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China
raw_author_name: Bangcheng Zhan
raw_affiliation_string: School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China

Abstract: 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

Authors:
raw_author_name: Alexander Wikner
raw_affiliation_string: Department of Physics, University of Maryland, 4150 Campus Dr, 20742, College Park, United States
raw_author_name: Michelle Girvan
raw_affiliation_string: Department of Physics, University of Maryland, 4150 Campus Dr, 20742, College Park, United States
raw_author_name: Brian R. Hunt
raw_affiliation_string: Department of Mathematics, University of Maryland, 4176 Campus Dr, 20742, College Park, United States
raw_author_name: Andrew Pomerance
raw_affiliation_string: Potomac Research LLC, 801 N Pitt St, 22341, Alexandria, United States
raw_author_name: Thomas Antonsen
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Maryland, 8223 Paint Branch Dr, 20742, College Park, United States; Department of Physics, University of Maryland, 4150 Campus Dr, 20742, College Park, United States
raw_author_name: Edward Ott
raw_affiliation_string: Department of Electrical and Computer Engineering, University of Maryland, 8223 Paint Branch Dr, 20742, College Park, United States; Department of Physics, University of Maryland, 4150 Campus Dr, 20742, College Park, United States

Abstract: 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

Authors:
raw_author_name: Chuang Niu
raw_affiliation_string: Department of Biomedical Engineering, School of Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Center for Computational Innovations, Rensselaer Polytechnic Institute, Troy, Ny, USA
raw_author_name: Junping Zhang
raw_affiliation_string: the School of Computer Science, Shanghai Key Laboratory of Intelligent Information Processing, Fudan University, Shanghai, China
raw_author_name: Ge Wang
raw_affiliation_string: Department of Biomedical Engineering, School of Engineering, Biomedical Imaging Center, Center for Biotechnology and Interdisciplinary Studies, Center for Computational Innovations, Rensselaer Polytechnic Institute, Troy, Ny, USA
raw_author_name: Hongming Shan
raw_affiliation_string: MOE Frontiers Center for Brain Science, Institute of Science and Technology for Brain-Inspired Intelligence, Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Shanghai

Abstract: 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

Authors:
raw_author_name: Ola Najah Kadhim
raw_affiliation_string: Al-Furat Al-Awsat Technical University
raw_author_name: Mohammed Hasan Abdulameer
raw_affiliation_string: University of Kufa

Abstract: 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%).
Hardware-efficient autonomous error correction with linear couplers in superconducting circuits

Authors:
raw_author_name: Ziqian Li
raw_affiliation_string: Department of Applied Physics, Stanford University, Stanford, California 94305, USA; Department of Physics, University of Chicago, Chicago, Illinois 60637, USA; James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA
raw_author_name: Tanay Roy
raw_affiliation_string: Department of Physics, University of Chicago, Chicago, Illinois 60637, USA; James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA
raw_author_name: David Rodríguez Pérez
raw_affiliation_string: Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA
raw_author_name: David I. Schuster
raw_affiliation_string: Department of Applied Physics, Stanford University, Stanford, California 94305, USA; Department of Physics, University of Chicago, Chicago, Illinois 60637, USA; James Franck Institute, University of Chicago, Chicago, Illinois 60637, USA; Pritzker School of Molecular Engineering, University of Chicago, Chicago, Illinois 60637, USA
raw_author_name: Eliot Kapit
raw_affiliation_string: Department of Physics, Colorado School of Mines, Golden, Colorado 80401, USA

Abstract: 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

Authors:
raw_author_name: None Musawer HAKIMI
raw_affiliation_string: Samangan University, Afghanistan

Abstract: 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

Authors:
raw_author_name: Rusul Hussein Hasan
raw_author_name: Inaam Salman Aboud

Abstract: 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.
Pelatihan Penggunaan Aplikasi Publish Or Perish (POP) Untuk Mencari Referensi Jurnal Pada Mahasiswa Universitas Muhammadiyah Jambi

Authors:
raw_author_name: Rian Dani
raw_author_name: Deka Veronica

Abstract: 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

Authors:
raw_author_name: None Asep Purwo Yudi Utomo
raw_author_name: None Didi Pramono

Abstract: 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

Authors:
raw_author_name: None Abd. Rahman Rahim
raw_author_name: None Haslinda Haslinda

Abstract: 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.
The Effectiveness of two Dimensional Haar Wavelet Image De-noising performance using Soft or Hard Thresholding Approach

Authors:
raw_author_name: Khamees Khalaf Hasan
raw_affiliation_string: College of Engineering Al-Shirqat, Tikrit university, Al-Shirqat, Iraq

Abstract: Image de-noising and restoration represent basic problems in image processing with many different applications including engineering, reconstruction of missing data during their transmission and enhancement ..etc. this work is aimed at developing effective algorithm for denoising image using new strategy algorithm of wavelet techniques ,by applying two dimensions wavelet transform using Haar wavelet. Wavelets are hierarchically decomposing mathematical tools. A noisy picture is sent to the Haar wavelet transform to create four decomposed bands(for each level), and the noise is then removed by applying a threshold to the resultant image band. Then, an inverse transform is used to provide a noise-reduced picture. Performance characteristics are improved by the suggested design in contrast to those of the current approaches.
Research on low-light image enhancement algorithm based on filtering and data-driven composite

Authors:
raw_author_name: Zhengdong Cheng
raw_affiliation_string: Advanced Laser Technology Laboratory of Anhui Province (China); National Univ. of Defense Technology (China)
raw_author_name: Yi Chen
raw_affiliation_string: Advanced Laser Technology Laboratory of Anhui Province (China); National Univ. of Defense Technology (China)
raw_author_name: Bo Xie
raw_affiliation_string: Advanced Laser Technology Laboratory of Anhui Province (China); National Univ. of Defense Technology (China)

Abstract: Low-light images have low visibility, poor contrast, and weakened details, which pose significant obstacles for subsequent computer vision tasks. When enhancing low-light images, multiple factors such as brightness, contrast, artifacts, and noise need to be considered, making this problem challenging. Low-light enhancement algorithms based on the Retinex theory are important methods in the field of image enhancement, with a wide range of applications and practicality. In this paper, starting from the low-light image imaging model, we address the issues of color distortion and limited brightness improvement exhibited by traditional Retinex-based algorithms and deep learning algorithms in actual low-light scenarios. We propose a filtering and data-driven composite enhancement algorithm (FADDC) that performs weighted fusion on output and intermediate quantities before outputting them. Experimental results combining SCI-Net with traditional algorithms SSR and MSR demonstrate that this fusion method can effectively enhance image brightness without color distortion, achieving better enhancement results.
<i>Is Gravity a Force or Not? A Tale of Scientific Models</i>

Authors:
raw_author_name: Dan MacIsaac
raw_affiliation_string: Physics Department, SUNY-Buffalo State University , Buffalo, NY 14222; macisadl@buffalostate.edu

Abstract: Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Twitter Facebook Reddit LinkedIn Tools Icon Tools Reprints and Permissions Cite Icon Cite Search Site Citation Dan MacIsaac; Is Gravity a Force or Not? A Tale of Scientific Models. Phys. Teach. 1 February 2024; 62 (2): 158. https://doi.org/10.1119/10.0024604 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAmerican Association of Physics TeachersThe Physics Teacher Search Advanced Search |Citation Search
Digitalisation of Banking as a Catalyst for Inter-Country Money Transfers in Africa

Authors:
raw_author_name: Ranson Sifiso Gwala
raw_affiliation_string: University of KwaZulu-Natal, South Africa
raw_author_name: Pfano Mashau
raw_affiliation_string: University of KwaZulu-Natal, South Africa

Abstract: The purpose of this research was to examine how banking digitalisation acts as a catalyst for money transfers and economic development between countries of origin and country of labour migration in Africa. This chapter used desktop research to understand how improvements in banking digitalisation using different platforms is transforming African movement of money and saving money senders a lot of money and time in inter account transfers. M-PESA; Mukuru, supermarket money market transfers, bank transfers have been dominant FinTech innovations transforming and improving quality of life and fostering financial inclusion within the African continent and diaspora. This chapter aids in understanding how digitalisation assists ordinary citizens, especially the poor who need to save every rand or dollar that they can save in order to make meaningful change in their lives and the lives of their loved ones anywhere in Africa and the world under these trying post-COVID 19 economic conditions.