Reviewerly/exHarmony huggingface dataset:
Analisis Manfaat Penerapan Basis Data dalam Lingkungan Sekolah di Indonesia

The aim of this study to provide the importance of the benefits of databases in schools today, because in this sophisticated era, database needs are needed to make it easier to make reports, retrieve and store data that was originally made manually which is time consuming and prone to data errors. . With the application of a database system, everything can be done easily and quickly, and the resulting data will be accurate. The research method used is a qualitative method by observing and recording reality through observation, interviews, and documentation. The results of the study show that the benefits of using databases in schools today are the quality of the data and information accessed according to what is input, and the most important benefit is classifying data and information so that it is easier for teachers to understand, anticipating the occurrence of data similarities or data instability. Helping the process of saving, accessing, editing, and deleting data, Protecting databases for schools to shorten processing time and get significant results.&#x0D; Keywords: Database, School, Benefits of Database
Secured Smart Manufacturing Systems Using Blockchain Technology for Industry 4.0

Industry 4.0, which involves the automation of core operations, aims to build an intelligent, connected, and reliable supply chain. An essential aspect of information security, the reliability of the systems was based on cordial relations, assurances, openness, and accountability. Specialists in a variety of areas of IT have become interested in blockchain technology as a safe and affordable setting to monitor many company productions. Cyber-Physical Smart Manufacturing Systems (CPSMS) were advanced production technologies that strive to synchronize and link the hardware reality of machines with the digital domain. In addition, for CPSMS to be used in the real world, extended interconnection and a computing framework were essential. The unified 3 blockchain design proposed in this study serves as a framework for experts and businesses to properly recognize the possibilities of blockchain to adjust, expand, and integrate this innovation with their industrial advancements towards Industry 4.0. Additionally, the federated learning-enabled technology offers safe power exchange between many crucial organizations. Reliability assessments are performed to evaluate the proposed solution’s performance in terms of reliability and responsiveness in a scenario involving an energy exchange network for vehicles.
Homodyne-detection-based discrete-variable quantum key distribution with a heralded single-photon source

Discrete-variable quantum key distribution (DV-QKD) has recently been implemented using a homodyne detection system, and a notable secret key rate can be achieved by employing an ideal single-photon source. However, most QKD implementations employ practical light sources, including a phase-randomized weak coherent source and a heralded single-photon source, which occasionally produce multiphotons and are vulnerable to photon-number-splitting (PNS) attacks. In this work, we propose a three-decoy-state method using a heralded single-photon source for homodyne-detection-based DV-QKD, thus making it immune to PNS attacks with current technology. Our simulation results demonstrate that our proposed protocol can achieve high-speed and secure key distribution over metropolitan distances. Our work paves a cost-effective path to realize DV-QKD and further incorporate it into classical telecommunication networks.
Aplikasi Virtual Reality 3 Dimensi Sebagai Media Promosi Waterboom XYZ

Currently, the tourism sector is one of the sectors affected in terms of income caused by the COVID-19 pandemic, one of the things that can help the tourism sector at this time is, with information media carried out to promote the tourism sector. To overcome the problems above, tools are needed to help the process mentioned above, and the tool recommended by the author is to make an Android-based virtual reality application, so it is hoped that by using the virtual reality application, people will become more interested because they can feel the situation around them. which is similar to the real situation before visiting a tourist place. In this study the authors took a case study in one of the tourism places in Banten, especially in Lebak Regency, namely Waterboom XYZ. In the development of this virtual reality application, the method used is the Multimedia Development Life Cycle (MDLC) and the tools used in the development of this virtual reality application use the C# programming language provided in Unity 3D and the Blender application in making 3D modeling. From the test results on the system that has been built, it is found that the system has had a performance that is in accordance with the user's needs along with the results of the User Acceptance Test which received a positive response with a percentage of 82.80%.
21st Century Language Learners' Perceptions and Expectations of Self-Directed Language Learning Apps to Enhance their Listening Skills

It is now very impressive how technology has transformed the way 21st-century learners learn and acquire new language skills through listening lessons. It was from listening to the audio that teachers played using a CD player during language lessons, to using headphones and getting the chance to listen to audio or even videos available on various online language learning platforms or mobile applications. It was from the traditional method that used pen and paper to answer listening comprehension questions to only clicking their choice of answers on the mobile phone screen. In today's world, where students' lives cannot be separated from technology, students not only have the chance to get access to millions of authentic audio and videos online from all over the world but also have the freedom and flexibility to pause, stop or replay the video according to their needs which gives the learners the freedom to control and self-direct their own learning. Self-directed learning refers to a teaching approach where students take more responsibility for their own learning and take an active role in setting their own goals, seeking out resources and materials, and evaluating their own progress. Keywords: self-directed learning, mobile applications, teaching listening skills
Who can Submit an Excellent Review for this Manuscript in the Next 30 Days? - Peer Reviewing in the Age of Overload

With millions of research articles published yearly, the peer review process is in danger of collapsing, especially in “hot” areas with popular conferences. Challenges arise from the large number of manuscripts submitted, skyrocketing use of preprint archives and institutional repositories, problems regarding the identification and availability of experts, conflicts of interest, and bias in reviewing. Such issues can affect the integrity of the reviewing process as well as the timeliness, quality, credibility, and reproducibility of research articles. Several solutions and systems have been suggested, but none work well, and neither authors nor editors are happy with how long it takes to complete reviewing the submitted research. This panel addresses these challenges and potential solutions, including digital libraries that recommend reviewers, as well as broader issues like opportunities for identifying peer reviewers for scholarly journals by engaging doctoral students and postdocs, as well as those who recently completed their Ph.D.
JCDL2023 Workshop: Innovation Measurement for Scientific Communication (IMSC) in the Era of Big Data

The increasingly mature artificial intelligence technologies, such as big data, deep learning, and natural language processing, provide technical support for research on automatic text understanding and bring development opportunities for innovative measurement of scientific communication. Innovation measurement in scientific communication is a challenging and cutting-edge direction in Informetrics. It is interdisciplinary, requiring considering the characteristics of different disciplines and different types of scientific outcomes to establish a comprehensive evaluation metrics system. On the other hand, metadata and content features should be considered to reflect the innovation of scientific works objectively and comprehensively. This workshop focuses on the discussion and produces enlightening outcomes. We will engage broad audiences to share their ideas and pre-productions, enabling an interdisciplinary approach to exploring frontier areas. This workshop consists of keynotes, oral presentations, and poster sessions and would attract interest from academic researchers, librarians, and decision-makers from governments and practical sectors.
Research on Autonomous Obstacle Avoidance for Robot Cluster under Communication Distance Constraint

In order to avoid obstacles under the constraint of communication distance, the individual control protocol of robot cluster is designed, and the stability of the system is proved based on Lyapunov stability theory. The proposed robot cluster self-organizing obstacle avoidance control strategy is verified by simulation. The results show that the designed control protocol can ensure that the robots that initially meet the communication distance constraints always stay in the communication range and successfully achieve obstacle avoidance.
Human–Computer Interactions with Artificial Intelligence and Future Trends of HCI—A Study

Artificial Intelligence, the name itself depicts the meaning that providing the knowledge of human to the machine artificially. AI is not a sense or feeling but the software or a model evolved to do complex tasks like human beings. With the invention of computer it has become so easy to do day to day jobs without much effort. HCI is all about interacting with computers. Now-a days it is possible to mesh with the computer through voice, touch, eye movement, and hand gestures. HCI has many challenges but has established in grand manner with the support of Artificial Intelligence. This study provides some important roles of Artificial Intelligence in HCI and its future development.
Methodology for Integrating Variables for a Transdisciplinary Productivity Model

This paper describes the stages for the formulation of a transdisciplinary productivity model based on the integration of previously sele1cted exact, social, and human variables in doctoral research. The set of social factors are classified according to the identity of people and their interaction in a group environment. Exact variables are related to the processes, their resources, and the government. Soft sciences variables constitute the differential of the construct with respect to the traditional models of productivity measurement, structured based on exact variables. Stages form the developed methodology. Results include obtaining the relevant variables from the correlation of the aggregate values of the applied validation and the formulation of the model protocol. The mathematical modeling is under development.
Impact of Digital Transformation of the Russian Higher Education System on the Need to Develop Competencies and Career Advancement of Scientific and Pedagogical Employees

Introduction. Distance learning, caused by the sanitary and epidemiological situation in the world, has led to the need to restructure the higher education system from the traditional full-time/offline attendance to a distance format. The use of the “newˮ format for higher education assumed the use of digital technologies, therefore there was a need for the formation of appropriate digital and “soft” competencies among the teaching and research staff of higher educational institutions. The purpose of the article is to analyze the satisfaction of teaching and research staff of Russian higher educational institutions with the system of professional and official promotion, their assessment of the need for general professional, organizational and managerial, information communication, regulatory legal and interpersonal competencies in the conditions of digital transformation of the higher education system of the Russian Federation. Materials and Methods. To identify the opinion of scientific and pedagogical workers about changes in the importance of competencies, the development of a business career, the authors conducted a sociological survey of 1,532 scientific and pedagogical workers of higher education. The monitoring was carried out taking into account methodological approaches developed with the participation of the scientific and educational laboratory of the Penza affiliated branch of the Financial University “Strategies for increasing the competitiveness of international activities of universities in the global digital space”. Results. According to the results of the sociological survey, a low level of satisfaction of scientific and pedagogical staff was revealed, caused by the presence of the following problem areas: the prevalence of paper reporting over the scientific and pedagogical activities of employees; excessive bureaucracy that hinders professional and official development; an incorrect system of performance indicators; the absence of a unified career development model for scientific and pedagogical staff of educational institutions. Practice-oriented recommendations have been developed in the field of digital and “softˮ competencies, the formation of a management system for the professional development of scientific and pedagogical staff. Discussion and Conclusion. The formulated theses contribute to the development of theoretical and methodological approaches to the study of the level of satisfaction of scientific and pedagogical staff of Russian universities. The presented materials of the article can be used for research purposes by scientists, researchers dealing with similar issues, as well as federal and regional executive authorities, higher educational organizations in the development of professional development programs for teaching staff.
OW-SLR: overlapping windows on semi-local region for image super-resolution

There has been considerable progress in implicit neural representation to upscale an image to any arbitrary resolution. However, existing methods are based on defining a function to predict the RGB value from just four specific loci. Relying on just four loci is insufficient as it leads to losing fine details from the neighboring region(s). We show that by taking into account the semi-local region leads to an improvement in performance. In this paper, a new technique called Overlapping Windows on Semi-Local Region (OW-SLR) is applied to an image to obtain any arbitrary resolution by taking the coordinates of the semi-local region around a point in the latent space. This extracted detail is used to predict the RGB value of a point. We illustrate the technique by applying the algorithm to the Optical Coherence Tomography-Angiography (OCT-A) images and show that it can upscale them to random resolution. This technique outperforms the existing state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides better results for classifying healthy and diseased retinal images such as diabetic retinopathy and normals from the given set of OCT-A images. The project page is available at https://rishavbb.github.io/ow-slr/index.html
Lifelong context recognition via online deep clustering

Until recently, evaluating the quality of unsupervised learning was too slow and expensive. This was a major hurdle to edge-enabled AI and any situations for which computational expense is a significant requirement. Adaptive Resonance Theory has been part of the solution because it can self-correct based on unsupervised category mismatch detection and reset. This advantage can be further leveraged by the development of incremental cluster validity indices. Validity indices provide various quality measures for unsupervised learning. Converting these to incremental versions is an approach that dominates prior methods, particularly for real-time or edge computing applications. Integrating incremental measures into the machine learning architecture further enhances these cost and speed advantages.
LocNet: deep learning-based localization on a rotating point spread function with applications to telescope imaging

Three-dimensional (3D) point source recovery from two-dimensional (2D) data is a challenging problem with wide-ranging applications in single-molecule localization microscopy and space-debris localization telescops. Point spread function (PSF) engineering is a promising technique to solve this 3D localization problem. Specifically, we consider the problem of 3D localization of space debris from a 2D image using a rotating PSF where the depth information is encoded in the angle of rotation of a single-lobe PSF for each point source. Instead of applying a model-based optimization, we introduce a convolution neural network (CNN)-based approach to localize space debris in full 3D space automatically. A hard sample training strategy is proposed to improve the performance of CNN further. Contrary to the traditional model-based methods, our technique is efficient and outperforms the current state-of-the-art method by more than 11% in the precision rate with a comparable improvement in the recall rate.
A Temporal Learning Approach to Inpainting Endoscopic Specularities and Its Effect on Image Correspondence

Video streams are utilised to guide minimally-invasive surgery and diagnosis in a wide range of procedures, and many computer-assisted techniques have been developed to automatically analyse them. These approaches can provide additional information to the surgeon such as lesion detection, instrument navigation, or anatomy 3D shape modelling. However, the necessary image features to recognise these patterns are not always reliably detected due to the presence of irregular light patterns such as specular highlight reflections. In this paper, we aim at removing specular highlights from endoscopic videos using machine learning. We propose using a temporal generative adversarial network (GAN) to inpaint the hidden anatomy under specularities, inferring its appearance spatially and from neighbouring frames, where they are not present in the same location. This is achieved using in-vivo data from gastric endoscopy (Hyper Kvasir) in a fully unsupervised manner that relies on the automatic detection of specular highlights. System evaluations show significant improvements to other methods through direct comparison and ablation studies that depict the importance of the network's temporal and transfer learning components. The generalisability of our system to different surgical setups and procedures was also evaluated qualitatively on in-vivo data of gastric endoscopy and ex-vivo porcine data (SERV-CT, SCARED). We also assess the effect of our method in comparison to other methods on computer vision tasks that underpin 3D reconstruction and camera motion estimation, namely stereo disparity, optical flow, and sparse point feature matching. These are evaluated quantitatively and qualitatively and results show a positive effect of our specular inpainting method on these tasks in a novel comprehensive analysis. Our code and dataset are made available at https://github.com/endomapper/Endo-STTN.
Media Usee-Learning Scorm Standards In Increasing Teacher's Pedagogic Competence At SDN 008 Rokan IV Koto, Rokan Hulu District

Teachers have more limited time to improve their abilities than transferring material to students. It is hoped that by using the mediae-learning with SCORM standards, teacher competence can increase, so that the quality of students will indirectly improve. The purpose of this study was to determine differences in the ability of teachers to develop teaching materials on aspects of knowledge between those using the mediae-learning SCORM standard with modules. The method used in this research is a quasi-experimental designcontrol group pre-test post-test. The results of the study show that there is an influence on the use of mediae - learning SCORM standards for improving teacher pedagogic competence at SDN 008 Rokan IV Koto. Specifically, the conclusions are as follows: (1) Media usee-learning having a SCORM standard has a significant effect on the teacher's ability to develop teaching materials for knowledge aspects; (2) Use of mediae-learning having a SCORM standard has a significant effect on the teacher's ability to develop teaching materials for understanding aspects; (3) Use of mediae-learning SCORM standards have no significant effect on the teacher's ability to develop teaching materials for the application aspect
FreqSense: Adaptive Sampling Rates for Sensor-Based Human Activity Recognition Under Tunable Computational Budgets

Recent years have witnessed great success of deep convolutional networks in sensor-based human activity recognition (HAR), yet their practical deployment remains a challenge due to the varying computational budgets required to obtain a reliable prediction. This paper focuses on adaptive inference from a novel perspective of signal frequency, which is motivated by an intuition that low-frequency features are enough for recognizing “easy” activity samples, while only “hard” activity samples need temporally detailed information. We propose an adaptive resolution network by combining a simple subsampling strategy with conditional early-exit. Specifically, it is comprised of multiple subnetworks with different resolutions, where “easy” activity samples are first classified by lightweight subnetwork using the lowest sampling rate, while the subsequent subnetworks in higher resolution would be sequentially applied once the former one fails to reach a confidence threshold. Such dynamical decision process could adaptively select a proper sampling rate for each activity sample conditioned on an input if the budget varies, which will be terminated until enough confidence is obtained, hence avoiding excessive computations. Comprehensive experiments on four diverse HAR benchmark datasets demonstrate the effectiveness of our method in terms of accuracy-cost tradeoff. We benchmark the average latency on a real hardware.
Application Of The M-Apos Learning Model (Modification-Action, Process, Object, Scheme) To Improve Mathematics Outcomes In Class IV State Elementary School 011 Rambah Samo

This study aims to find out that the application of M-APOS learning to improve mathematics learning outcomes for fourth grade students at SD Negeri 011 Rambah Samo. The background that underlies this research is the failure to achieve the learning objectives as illustrated by the students' daily test scores in mathematics learning which are still below the KKM score of 75. The subjects in this class action research were 29 students of SD Negeri 011 Rambah Samo, consisting of 14 female students and 15 male students. The data analysis technique used in this study was to use several instruments in the form of question sheets and student activity observation sheets. The results of this study can be seen from the first cycle of students who completed 50% (17 students) and 43% (11 students) did not complete with an average score of 65.30, increased by 60% (19 students) ). , then experienced an increase in meeting 2, with 92% (22 students) completing and 8% (7 students) not completing with an average score of 90.5. With an increase in learning outcomes there is an increase in the activity of teachers and students. Based on these learning outcomes it can be said that there was an increase in student learning outcomes from cycle 1 to cycle 2 by 35%. When viewed from the data that has been obtained, it can be seen that those who passed have exceeded the indicators of success. So it can be concluded that the application of M-APOS learning to improve Mathematics learning outcomes for fourth grade students at SD Negeri 011 Rambah Samo has been successful.