Reviewerly/exHarmony huggingface dataset:
Robot Human-Machine Interaction Method Based on Natural Language Processing and Speech Recognition

With the rapid development of artificial intelligence technology, robots have gradually entered people's lives and work. The robot human-machine interaction system for image recognition has been widely used. However, there are still many problems with robot human-machine interaction methods that utilize natural language processing and speech recognition. Therefore, this study proposes a new robot human-machine interaction method that combines structured perceptron lexical analysis model and transfer dependency syntactic analysis model on the basis of existing interaction systems. The purpose is to further explore language based human-machine interaction systems and improve interaction performance. The experiment shows that the testing accuracy of the structured perceptron model reaches 95%, the recall rate reaches 81%, and the F1 value reaches 82%. The transfer dependency syntax analysis model has a data analysis speed of up to 750K/s. In simulation testing, the new robot human-machine interaction method has an accuracy of 92% compared to other existing methods, and exhibits excellent robustness and response sensitivity. In summary, research methods can provide a theoretical and practical basis for the improvement of robot interaction capabilities and the further development of human-machine collaboration.
Estimation of the Parameters of Free Space Quantum Key Distribution System Depending on the Insertion Losses

A telescopic system consisting of two modules is used to organize the quantum channel. The accuracy of the adjustment of these modules relative to each other ensures a reduction in losses in the channel, therefore, an important step before conducting the next experiments is the alignment of the system. Both modules of the system are mounted on movable supports that allow the modules to rotate around two axes. Using these supports, we managed to reduce the losses in the atmospheric channel with a length of 1 meter to 10 dB when the radiation is introduced into a single-mode fiber.
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement

Low-light image enhancement tasks demand an appropriate balance among brightness, color, and illumination. While existing methods often focus on one aspect of the image without considering how to pay attention to this balance, which will cause problems of color distortion and overexposure etc. This seriously affects both human visual perception and the performance of high-level visual models. In this work, a novel synergistic structure is proposed which can balance brightness, color, and illumination more effectively. Specifically, the proposed method, so-called Joint Correcting and Refinement Network (JCRNet), which mainly consists of three stages to balance brightness, color, and illumination of enhancement. Stage 1: we utilize a basic encoder-decoder and local supervision mechanism to extract local information and more comprehensive details for enhancement. Stage 2: cross-stage feature transmission and spatial feature transformation further facilitate color correction and feature refinement. Stage 3: we employ a dynamic illumination adjustment approach to embed residuals between predicted and ground truth images into the model, adaptively adjusting illumination balance. Extensive experiments demonstrate that the proposed method exhibits comprehensive performance advantages over 21 state-of-the-art methods on 9 benchmark datasets. Furthermore, a more persuasive experiment has been conducted to validate our approach the effectiveness in downstream visual tasks (e.g., saliency detection). Compared to several enhancement models, the proposed method effectively improves the segmentation results and quantitative metrics of saliency detection. The source code will be available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/woshiyll/JCRNet</uri> .
GEMA: A Genome Exact Mapping Accelerator Based on Learned Indexes

In this paper, we introduce GEMA, a genome exact mapping accelerator based on learned indexes, specifically designed for FPGA implementation. GEMA utilizes a machine learning (ML) algorithm to precisely locate the exact position of read sequences within the original sequence. To enhance the accuracy of the trained ML model, we incorporate data augmentation and data-distribution-aware partitioning techniques. Additionally, we present an efficient yet low-overhead error recovery technique. To map long reads more efficiently, we propose a speculative prefetching approach, which reduces the required memory bandwidth. Furthermore, we suggest an FPGA-based architecture for implementing the proposed mapping accelerator, optimizing the accesses to off-chip memory. Our studies demonstrate that GEMA achieves up to 1.36× higher speed for short reads compared to the corresponding results reported in recently published exact mapping accelerators. Moreover, GEMA achieves up to ∼22× faster mapping of long reads compared to the available results for the longest mapped reads using these accelerators.
BDAL: Balanced Distribution Active Learning for MRI Cardiac Multistructures Segmentation

Various kinds of heart diseases pose a serious threat to human health. To effectively treat and prevent these diseases, accurate segmentation of the entire heart structure is crucial for medical research and application. At present, the solution to this problem still needs to rely on a lot of manpower. Not only is this time-consuming, but accuracy is sometimes difficult to guarantee. In the deep learning methods for medical image segmentation, large labeled images are difficult to obtain. Typically, the large databases have several thousand images, of which only a few hundred have been annotated, and the number of individual patients is even smaller. In this article, we focus on a small part of the dataset to minimize the cost of manual labeling and maximize the accurate segmentation results. The small part of the dataset contains more representative and informative images, avoiding doctors to repeatedly label images with similar information. We proposed a balanced distribution active learning (BDAL) framework for MRI cardiac multistructures segmentation based on reinforcement learning. The deep Q-network framework can learn an effective policy to select some informative and representative images to be labeled from a large number of the unlabeled dataset. We consider the shape features of images and the balance of different class distributions to build new state and action representation, which can help the agent to identify informative and representative images for annotation. Our BDAL method provides an agent to improve the ability of AL to select images to improve the accuracy of segmentation. Moreover, experiments and results show that our BDAL method significantly outperforms all baselines and other AL-based methods under the same amount of annotation budget on MRI cardiac multistructures segmentation in datasets <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {ACDC}$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathbf {M}\&amp;\mathbf{Ms}$</tex-math></inline-formula> .
Seismic hazard assessment for areas of volcanic activity in western Kingdom of Saudi Arabia

First posted December 29, 2023 For additional information, contact: Volcano Science Center - Menlo ParkU.S. Geological Survey345 Middlefield Road, MS 910Menlo Park, CA 94025Contact Pubs Warehouse Earthquake swarms caused by volcanic activity, tectonic stresses, or industrial operations (oil and gas production) can pose considerable risk for nearby settlements. As a rule, a probabilistic seismic hazard assessment (PSHA) that is based on time-independent earthquakes does not take into account earthquake swarms because of their statistically time-dependent nature. We describe the technique and application of a scenario-based method for the estimation of seismic hazard from earthquake swarms that may occur in areas of volcanic activity in western Saudi Arabia. The method consists of the generation of a large number of scenario seismic swarms followed by the calculation of ground motion in a site of interest for all earthquakes in all of the swarms. The set of calculated ground-motion values permits the construction and analysis of probability distribution functions for possible ground-motion levels during the hypothetical earthquake swarms. The level of ground motion representing the hazard is selected considering an appropriate value of the probability of exceedance or non-exceedance, depending on the goal of the study. This swarm-scenario-based seismic hazard assessment may provide a valuable supplement to an ordinary PSHA and (or) deterministic seismic hazard assessment that are commonly used for emergency response and post-earthquake recovery management.
7 Hands-on Strategies for Struggling Readers

Struggling readers are found in almost every classroom across the world. With differing learning styles and abilities, teachers are encouraged now, more than ever, to be innovative when teaching foundational reading strategies. Within this article, readers are provided with a literature review of research and educational literature that discusses how multisensory, hands-on activities promote engagement and active learning for all students. The recommended seven hands-on learning strategies that can promote learning and support for struggling readers during literacy instruction include Build the Words, Feel the Words, Whole Body Letters, Five Finger Retell, Sight Word BINGO, Elkonin Boxes, and Word Swat.
Needs Assessment for Establishment of Telepathology in the Philippines

Objective. The Philippines has more than a hundred ten million population with a very limited number of general pathologists and subspecialist pathologists. Consultation of pathologists with other pathologists is important to ensure accurate results for difficult cases. However, pathologists are not always accessible to review slides. Telepathology can provide access to other pathologists by sending microscopic images through the internet. This study explores the needs of pathologists for consultation in their practice that may be aided by telepathology. The status of current pathology practice and subspecialty consultations across the different regions in the Philippines were determined and the readiness of pathologists for telepathology was identified. Methodology. This is a cross-sectional descriptive study using an 18-item online survey questionnaire based on the World Health Organization guidelines on needs assessment for medical devices. The survey was distributed among anatomic pathologists practicing in the Philippines. Results. One hundred forty (140) pathologists responded and were included in this study. 5-10% of cases of respondents required subspecialty referral. Diagnostically challenging cases and confirmation of malignancy are the most common reasons for consultation. Respondents practicing outside the National Capital Region (NCR) have fewer subspecialist pathologists available for referrals within their region. Turnaround times for signing out challenging cases are longer outside NCR (&gt;7 days) compared to NCR (4-7 days). Most respondents have access to the basic equipment to perform telepathology, which includes, an internet link, a smartphone with high-resolution camera and a computer. Almost all respondents will use telepathology if it is available. Conclusion. A hub-and-spoke telepathology network can provide access to subspecialty consultation to reduce the diagnostic turnaround time and to increase the accuracy of results for challenging cases. The availability of the minimum telepathology infrastructure and the positive attitude of the pathologists towards telepathology may be indicators of readiness for a local telepathology system in the Philippines.
Postposition of the King"s Orders and Greetings, ‘No {ノ(乃/能)}’

Kana‘乃’ was imported by a naturalized person commented by Ono Susumu (おおのすすむ) in the early period of Empress Suiko, and adopted as a phonogram, i.e. only its reading was borrowed with no influence of its meaning, and used as a postposition. In the 2nd stage of Empress Suiko, “Manyosu”,”Kojiki” and “Japanese Historical Records” showed that it was recognized as kana in the 1st stage of Empress Suiko. It was diversely used as a main kana, and showed the highest frequency in transcription of a substantive (noun/adjective/verb) and an expletive (auxiliary verb/postposition). No particulars or notes of the postposition were found as onkana ‘(older type of kana)’ in smaller letters than those in main texts until Norito‘Shinto prayers’ and Senmyo ‘orders of an emperor/empress’.BR Next, kana ‘能’ was not shown in the literary of the 1st stage of Empress Suiko, original Shinto prayers or native songs of Shilla which were transcribed with Chinese characters based on the characters’ sounds. It is thus thought that the kana was used after kana ‘乃’. That is, it appeared in the 2nd stage of Empress Suiko, and was used as kana, and together with ‘乃’ showed the 1st and 2nd frequencies for writing ‘ノ’ for a substantive and an expletive. As such, it was selected as ongana to represent particulars for postposition in Norito and Senmyo.
Security Issues and Defensive Approaches in Deep Learning Frameworks

Abstract: Deep learning frameworks are instrumental in advancing artificial intelligence, showcasing vast potential across diverse applications. Despite their transformative impact, security concerns pose significant risks, impeding widespread adoption. Malicious internal or external attacks on these frameworks can have far-reaching consequences for society. Our research delves into the intricacies of deep learning algorithms, conducting a thorough analysis of vulnerabilities and potential attacks. To address these challenges, we propose a comprehensive classification system for security issues and corresponding defensive approaches within deep learning frameworks. By establishing clear connections between specific attacks and their defenses, we aim to enhance the robustness of these frameworks. In addition to theoretical considerations, our study extends to real-world applications, exploring a case involving the practical implications of deep learning security issues. Looking ahead, we discuss future directions and open issues within the realm of deep learning frameworks, aspiring to inspire further developments. We anticipate that our research will not only contribute valuable insights but also attract attention from both academic and industrial domains, fostering a collective commitment to fortifying the security of deep learning frameworks.
Mapping Undergraduate EFL Students’ Writing Anxiety: Trajectories from Responses, Reasons, and Strategies

Concerning the various responses of students’ anxiety on writing throughout the years, this sequential explanatory study aims to deeply explore the students’ writing anxiety and their reasons and strategies how to deal with it. The adapted questionnaire of Second Language Writing Anxiety Inventory and semi-structured interview were applied to examine students’ writing anxiety. Twenty five fourth semester students were sampled purposively, then they volunteered in the questionnaire and five of them were invited in the interview. The investigations revealed that among all types of anxiety, the students experienced most cognitive anxiety. The reasons for anxiety are linguistic difficulties, inadequate writing technique and practice, being afraid of tests, time pressure, and negative evaluation. The students’ strategies used for dealing with anxiety involve cognitive and affective aspects. These results can be a map in aiding students to cope with anxiety and suggests the important practical implications for teachers/lecturers and learners. Further, this study also discusses limitations and recommendations.
THE IMPACT OF TECHNOLOGICAL TRANSFORMATION ON CAREER CHOICES IN THE STEM SECTOR

This study aims to analyse the influence of digital transformation on career aspirations in the STEM (Science, Technology, Engineering, and Mathematics) sector. Qualitative and quantitative data analysis methods were used to collect data from various educational institutions and technology companies. The results show that the adoption of digital technology in STEM education increases students' interest and competence in STEM subjects, which directly affects their career aspirations. The research also found that digital skills acquired through STEM education contribute significantly to job readiness in the STEM sector. In addition, the findings highlight the importance of project-based and collaborative learning enriched with digital technologies to prepare students for the changing work environment. This research contributes to the literature by providing insights into how STEM education, accompanied by digital transformation, can shape career aspirations in the STEM sector. Recommendations for education practitioners and policymakers include greater integration of digital technologies in STEM curricula and the development of teacher training programmes to teach with advanced technologies. Implications for career development in the STEM sector are also discussed, emphasising the importance of continuity between education and industry needs.
EFL Students’ Learning Strategies During Covid-19 Pandemic: A Comparative Study Between Urban and Rural School Settings

The present research is a descriptive quantitative research design using a survey method. There are 30 items adapted and modified from the SILL (Strategy Inventory for Language Learning) theory used to measure students' strategies in learning English. There are two senior high school (SMA) and 2 junior high school (SMP) contexts or equivalent as the subject of this research, namely students who study in the city of Mataram (urban school setting) and outside the city of Mataram (rural school setting). There were 112 students who participated in this study. Data analysis was carried out by calculating the total score of each question to determine the percentage level of relevance of the statement to the student's condition. The results of this study indicate that the English learning strategies of students in the city and outside the city are almost the same where they use the six learning strategies. Affective strategies and metacognitive strategies remained as the most favorable learning strategies for the EFL students.
Elaboration of an Ontology to Address Women’s Presence on Computer Courses in Brazil

Women’s presence in STEM (Science, Technology, Engineering and Math) has been growing in relevance, yet research on the topic is met with a lack of data for the construction of consistent analysis. The Equality for Leadership in Latin American STEM Network (ELLAS) aims to develop a Linked Open Data (LOD) platform to help fill this gap. This work is embedded in ELLAS and contributes to (1) a triplication (RDF) of the data in Inep’s Higher Education Census, (2) the creation of a methodology for elaborating ontologies, to be used within the project, that enables an analysis of the presence and permanence of women in STEM areas and (3) its instantiation in the context of Higher Education in the field of computing in Brazil.
Education and Mathematics Models (A Case Study of Epidemiology of Virus Spread)

Viruses are the cause of diseases that affect the human body and can lead to pandemics and epidemics in various countries. No new content has been added beyond the original text. Viruses are the cause of diseases that affect the human body and can lead to pandemics and epidemics in various countries. The language used is clear, objective, and value-neutral, with a formal register and precise word choice. The text adheres to conventional structure and format, with consistent citation and footnote style. The text is free from grammatical errors, spelling mistakes, and punctuation errors. The purpose of this research is to determine the SIS model for the spread of viral diseases, such as Covid-19 and bird flu, and their resolution behavior. The model is formed by creating a flow diagram of the disease spread using the SIS (Susceptible, Infected, Susceptible) model. The sentences and paragraphs create a logical flow of information with causal connections between statements. The study revealed two equilibrium points: the disease-free equilibrium point and the endemic equilibrium point. To analyze the stability of the disease-free equilibrium point, linearization around the equilibrium point was used. The disease-free equilibrium point is asymptotically stable if the basic reproduction number is less than one, indicating that the disease will disappear after a certain period of time. Numerical simulations were conducted to analyze the behavior of the disease model.&#x0D; Keywords: Education Models, Mathematics Models, SIS (Susceptible, Infected, Susceptible)
EMPOWERING STUDENTS ACCESS CENTER (SAC) IN SUPPORTING ENGLISH LEARNING

This study focuses on conducting a need analysis to explore the role and effectiveness of the Student Access Center (SAC) in supporting English Education Department students' learning endeavors. The SAC is a place and an essential resource hub catering to students' academic, personal, and professional needs. The data was collected using a survey design from a questionnaire given to 51 English Education Department students at UIN Antasari Banjarmasin. The questionnaire included five Likert-scale-based choices regarding the student's learning needs about the role of SAC. The investigation delves into various aspects, including the collection of books provided by the SAC, its accessibility, and the extent to which it addresses students' needs both inside and outside the classrooms. The study examines students' perceptions of the SAC's facilities in enhancing their learning, the circulation service, and the function of SAC. Findings from this study contribute to a comprehensive understanding of the SAC's significance in supporting English Education Department students. Ultimately, this research aims to foster a conducive learning environment that maximizes students' academic achievements and success.