Reviewerly/exHarmony huggingface dataset:
ARTIFICIAL INTELLIGENCE TO IMPROVE ACCURACY AND EFFICIENCY OF PRESCRIBING BETWEEN DOCTORS &amp; PHARMACISTS

This qualitative method research aims to explain the use of artificial intelligence (AI) as an alternative means of optimizing interpersonal communication between doctors and pharmacists in terms of prescribing drugs. Because in practice, there are still many drug administration errors due to miscommunication between what the doctor writes or prescribes and what the pharmacist understands and gives to the patient. One that can be done by applying AI to electronic recipes. Unfortunately, the expansion of the use of AI technology in Indonesia in the pharmaceutical sector (especially drug prescribing) is still rarely found. In electronic prescribing, AI technology will help doctors to diagnose diseases suffered by patients through the analysis of previous medical reports. Then the doctor can directly send the prescription to the pharmacist through the computer. So that pharmacists can directly read, prepare and mix medicines. Through this mechanism, errors in prescribing that often occur can be minimized, thereby improving patient safety. The results of the study also show that although electronic prescribing can help overcome many medical problems, it can also cause new problems. For example electronic prescription systems can affect the workload of doctors and pharmacists by creating new types of prescribing errors such as delivery of prescriptions with medications, dosages or incorrect drug regimens. Not only that, but electronic prescribing systems can also raise concerns about information security and patient privacy when sharing prescription information with pharmacies. When sharing and storing patient prescription information, data security must be ensured to avoid changes that could potentially compromise the patient's health. The use of AI in electronic prescribing does not necessarily indicate clinical efficiency. Because it is also influenced by several factors, one of which is related to the knowledge and ability to use AI technology and the electronic prescribing system itself.
Innovative methodology for advanced structural condition assessment of tunnels

A methodology for holistic structural condition assessment of tunnels through high technological monitoring tools is proposed in this article.The methodology is divided into structural evaluation in real time through pre-installed equipment at the construction phase, and to post event structural evaluation through robotic inspection.The first part focuses on the static evaluation of the permanent tunnel lining after a strong earthquake in real time through fiber optic measurements.For this purpose, an improved type of fiber optic sensors is used, installed on inner and outer transversal reinforcing bars at eight points along the perimeter, allowing the recording of the time history of the elongation developed during the seismic action.A reformulation of the Parc-Ang criterion has been developed, allowing accurate automatic estimation of the degree of damage in each critical section according to the Moment-curvature diagram and the overall degree of indeterminacy of the structure.In the second part, the innovative model for crack analysis is described extensively, through which it is possible to calculate internal forces at the position of the structure where the crack is occurred, but also to estimate the possible locations of invisible cracks in the back face, based on the measurement of cracks' geometrical characteristics (widths, depths, angles and spacings) with sensors placed in robotic tools.Soil pressures and the internal forces N, V, M along the perimeter of the tunnel cross-section are calculated based on convergence measurements along 5 cords.The appropriateness of the methodology has been validated through the European Research Programs Tunneling, Monico, Robospect and Resist while the numerical results presented in this paper are obtained by application of robotic measurements in autonomous software packages implemented in JAVA programming language.
Special Issue for the 10th Anniversary of Axioms: Logic

Published for the first time in 2012, Axioms is celebrating its 10th anniversary [...]
Website Based Student Absence Application Design Using Extreme Programming Method

Absenteeism application is one of the systems where attendance is critical data in every activity, be it lecture activities. Nevertheless, often, the data - this absence of data is less noticed. The proof is that many educational institutions still use conventional absenteeism. Conditions like this are certainly very vulnerable to fraud. Therefore, the Informatics Engineering Study Program of the Faculty of Engineering needs a system that can create an absence process that can produce information more efficiently and by its standards, of course, using methods that can solve the problems in the Informatics Engineering Study Program of the Faculty of Engineering—using the Extreme Programming Method. The extreme Programming method was chosen because the software to be created is simple and classified as small-scale software, and development takes little time. This system development method is one of the agile methods. Which has several stages, namely: 1. Planning, 2. Design, 3. Coding, and 4. Testing. Of course, with some design processes that use UML, such as Use Case Diagram, Activity Diagram, Flowchart &amp;, and DFD, the student attendance website application built has been tested for eligibility using Blackbox testing. The results of this study create a system that can help informatics engineering programs to help in terms of student retirement. Design a system that can produce reports that suit needs effectively and efficiently.
Enabling Knowledge Refinement upon New Concepts in Abductive Learning

Recently there are great efforts on leveraging machine learning and logical reasoning. Many approaches start from a given knowledge base, and then try to utilize the knowledge to help machine learning. In real practice, however, the given knowledge base can often be incomplete or even noisy, and thus, it is crucial to develop the ability of knowledge refinement or enhancement. This paper proposes to enable the Abductive learning (ABL) paradigm to have the ability of knowledge refinement/enhancement. In particular, we focus on the problem that, in contrast to closed-environment tasks where a fixed set of symbols are enough to represent the concepts in the domain, in open-environment tasks new concepts may emerge. Ignoring those new concepts can lead to significant performance decay, whereas it is challenging to identify new concepts and add them to the existing knowledge base with potential conflicts resolved. We propose the ABL_nc approach which exploits machine learning in ABL to identify new concepts from data, exploits knowledge graph to match them with entities, and refines existing knowledge base to resolve conflicts. The refined/enhanced knowledge base can then be used in the next loop of ABL and help improve the performance of machine learning. Experiments on three neuro-symbolic learning tasks verified the effectiveness of the proposed approach.
Liver Disease Classification Using the Elbow Method to Determine Optimal K in the K-Nearest Neighbor (K-NN) Algorithm

Diagnosing liver disease in the field of healthcare is not an easy task. However, by utilizing medical records as datasets and applying data mining methods such as K-Nearest Neighbor (K-NN), we can analyze and extract knowledge automatically. The K-NN method has proven to be more effective compared to other methods as it clusters new information by selecting the nearest neighbors based on the value of k. In this study, we employed the Elbow method to determine the optimal value of k by measuring the error rate. The test results revealed that the optimal value of k was found to be 4, with the lowest error rate. In the third test, we achieved a training accuracy of 80.5% and a testing accuracy of 78.9%. After fine-tuning the training data, we were able to improve the accuracy to 82.2% for training and 77.1% for testing. However, in the fourth test, we encountered overfitting issues due to data imbalance caused by inappropriate resampling, resulting in a model that was overly complex and prone to excessive noise.
PENGARUH EDUKASI KESEHATAN PHBS DI SEKOLAH TERHADAP PENINGKATAN PENGETAHUAN SISWA KELAS 3 SD

Background: PHBS (Clean and Healthy Living Behavior) is a program that has been implemented by the government since 1996.However, until 2018 the number of PHBS implementation in Indonesia only reached 39.1%, this figure was still less than expected by the Ministry of Health, namely 65%.Research Objectives To determine the effect of PHBS health education in schools on increasing the knowledge of grade 3 students at SDN Bakalan Krajan2 Malang City Subjects and Methods: This research is a quantitative study using the pre-experiment design and the One Group Pre-test Post-test design.The population in this study is class 3A students with a total of 30 students.Sample in this study were 30 students in class 3A at SDN Bakalan Krajan2.And the Sampling technique in this study is Probability Sampling with Total Sampling.The sample in this study were 30 students in class 3A at SDN Bakalan Krajan2. Results:The results showed that knowledge before education was carried out by 30 respondents with an average score of 53 in the poor category, and knowledge after education on average 80 in the good category.The results of the analysis are with a significance of 0.001 <0.05. Conclusion:There is an increase in knowledge before and after being given treatment, namely by education about PHBS in schools.
Channel Regeneration: Improving Channel Utilization for Compact DNNs

Overparameterized deep neural networks have redundant neurons that do not contribute to the network's accuracy. In this paper, we introduce a novel channel regeneration technique that reinvigorates these redundant channels by re-initializing its batch normalization scaling factor gamma. This re-initialization of BN gamma promotes regular weight updates during training. Furthermore, we show that channel regeneration encourages the channels to contribute equally to the learned representation and further boosts the generalization accuracy. We apply our technique at regular intervals of the training cycle to improve channel utilization. The solutions proposed in previous works either raise the total computational cost or increase the model complexity. Integrating the proposed channel regeneration technique into the training methodology of efficient architectures requires minimal effort and comes at no additional cost in size or memory. Extensive experiments on several image classification and semantic segmentation benchmarks demonstrate the effectiveness of applying the channel regeneration technique to compact architectures.
Spatially-aware group interaction design framework for collaborative room-oriented immersive systems

Room-oriented immersive systems are human-scale built environments that enable collective multi-sensory immersion in virtual space. Although such systems are currently seeing increasing applications in public realms, limited understanding remains regarding how humans interact with the virtual environments displayed within. Synthesizing virtual reality ergonomics and human-building interaction (HBI) knowledge allows us to investigate these systems meaningfully. In this work, we develop a model of content analysis based on hardware components of the Collaborative-Research Augmented Immersive Virtual Environment Laboratory (CRAIVE-Lab) and the Cognitive Immersive Room (CIR) at Rensselaer Polytechnic Institute. Situating ROIS as a joint cognitive system, this model consists of five categories of qualitative factors: 1) general design approach; 2) topological relationships; 3) features of tasks; 4) hardware-specific design modalities; and 5) interactive qualities. We probe the comprehensiveness of this model using existing design scenarios at the CRAIVE-Lab and the CIR featuring both application-based and experience-based designs. Through these case studies, the robustness of this model in its representation of design intention is observed, with limitations on temporal constraints. In creating this model, we establish foundations for more detailed assessments of the interactive qualities of systems alike.
Management of the Emergent Literacy Program for Early Childhood at the Birrul Walidain Learning House

Early literacy is now an essential need in early childhood. Children must have initial reading and writing skills to be ready to enter the elementary school level. This study aimed to describe and analyze four management activities of an emergent literacy program in early childhood. This study is qualitative research classified as phenomenological research. The subjects of this study consisted of the managers, teachers, parents, and students of the Birrul Walidain learning house. This study applied interviews, observation, and documentation to collect the data. Then the data were analyzed using the data analysis techniques of the Miles and Huberman models, including data reduction, data presentation, and verification. The findings showed that the four management activities of the emergent literacy program in early childhood at the Birrul Walidain learning house are as follows: (1)planning the emergent literacy program; (2) organizing the emergent literacy program; (3) implementing an emergent literacy program by organizing initial reading and writing learning activities according to a predetermined schedule; (4) controlling the emergent literacy program. The results of this study had implications for the reconceptualization of emergent literacy programs in early childhood in terms of four managerial activities: planning, organizing, implementing, and controlling.
Robust One-Shot Segmentation of Brain Tissues via Image-Aligned Style Transformation

One-shot segmentation of brain tissues is typically a dual-model iterative learning: a registration model (reg-model) warps a carefully-labeled atlas onto unlabeled images to initialize their pseudo masks for training a segmentation model (seg-model); the seg-model revises the pseudo masks to enhance the reg-model for a better warping in the next iteration. However, there is a key weakness in such dual-model iteration that the spatial misalignment inevitably caused by the reg-model could misguide the seg-model, which makes it converge on an inferior segmentation performance eventually. In this paper, we propose a novel image-aligned style transformation to reinforce the dual-model iterative learning for robust one-shot segmentation of brain tissues. Specifically, we first utilize the reg-model to warp the atlas onto an unlabeled image, and then employ the Fourier-based amplitude exchange with perturbation to transplant the style of the unlabeled image into the aligned atlas. This allows the subsequent seg-model to learn on the aligned and style-transferred copies of the atlas instead of unlabeled images, which naturally guarantees the correct spatial correspondence of an image-mask training pair, without sacrificing the diversity of intensity patterns carried by the unlabeled images. Furthermore, we introduce a feature-aware content consistency in addition to the image-level similarity to constrain the reg-model for a promising initialization, which avoids the collapse of image-aligned style transformation in the first iteration. Experimental results on two public datasets demonstrate 1) a competitive segmentation performance of our method compared to the fully-supervised method, and 2) a superior performance over other state-of-the-art with an increase of average Dice by up to 4.67%. The source code is available at: https://github.com/JinxLv/One-shot-segmentation-via-IST.
Sinhala Inscription Character Recognition Model using Deep Learning Technologies

Nowadays archaeology experts put huge effort into extracting meaningful information manually from inscriptions. They take at least one month to identify a character. Characters have evolved into different shapes over the centuries. Archeology experts analyze all these shapes one by one to recognize a character. Reading inscriptions directly using manual procedure would be time-consuming and inefficient due to lack of inconsistency. Therefore, it is required to develop a modern technological solution to recognize ancient Sinhala inscription characters. With the purpose of that, this research mainly focuses on developing a solution using Optical Character Recognition module with information retrieval and storing functionality to recognize ancient Sinhala inscription characters which were mainly used in the century from 10 A.D TO 12 A.D. GIS techniques are used to present a map for inscription site tracking features that facilitate users to visit the locations of inscriptions. Mainly three OCR solutions were developed based on template matching, Artificial Neural Networks and Convolutional Neural Network separately. After evaluating each OCR solution, the best- resulted OCR solution was further implemented to incorporate into the Ancient Sinhala inscription character recognition system.
Proximal Stochastic Recursive Momentum Methods for Nonconvex Composite Decentralized Optimization

Consider a network of N decentralized computing agents collaboratively solving a nonconvex stochastic composite problem. In this work, we propose a single-loop algorithm, called DEEPSTORM, that achieves optimal sample complexity for this setting. Unlike double-loop algorithms that require a large batch size to compute the (stochastic) gradient once in a while, DEEPSTORM uses a small batch size, creating advantages in occasions such as streaming data and online learning. This is the first method achieving optimal sample complexity for decentralized nonconvex stochastic composite problems, requiring O(1) batch size. We conduct convergence analysis for DEEPSTORM with both constant and diminishing step sizes. Additionally, under proper initialization and a small enough desired solution error, we show that DEEPSTORM with a constant step size achieves a network-independent sample complexity, with an additional linear speed-up with respect to N over centralized methods. All codes are made available at https://github.com/gmancino/DEEPSTORM.
Literary and Tourist Project “OmskLiteraTur” as a Way to Preserve Cultural Tradition

The article traces the tendency of correlation between the preservation of a stable cultural tradition and the process of popularization of the literary heritage of the Omsk region through creation of a website, creation of new excursions, and systematization of literary content. The analysis of the conducted research allowed us to make a conclusion about the importance of information promotion. The project “OmskLiteraTur” allowed to draw attention to the problem of preservation and promotion of cultural heritage. In the course of the study, an entrance test was conducted for knowledge of the Omsk literary heritage, a register of literary excursion objects was compiled, scenarios of literary video tours were developed, interviews were taken, etc. The main emphasis in the research project was placed on young people as participants of the project, as a promising audience for broadcasting cultural traditions of a literary nature. Based on the study of the literary creativity of the Omsk region, conclusions were dawn about the process of the relationship between translation and preservation.
Aplikasi Pengelolaan Data Penduduk di Kantor RW Berbasis Web

The RW17 Citaliktik Soreang office is one of the Residents' Associations having its address at Kp. Citaliktik Soreang Islamic Boarding School with various kinds of resident activities. Currently the population data collection at the Citaliktik RW17 Office is to collect citizen data in a conventional manner according to its category. With this, it is necessary to implement an information system, namely the Population Data Collection Application which is implemented through the website. By utilizing a computer or laptop device, the system will be developed using object-oriented software development and programming (OOAD) methods. With the aim of being able to assist the RW17 Citaliktik Soreang Office in improving the existing system with the hope that the processing and presentation of population data which is still conventional can make it easier to make this population data collection application. The result of making the application is a Population Data Collection Application at the RW17 Citaliktik Soreang Office.
Self-Asymmetric Invertible Network for Compression-Aware Image Rescaling

High-resolution (HR) images are usually downscaled to low-resolution (LR) ones for better display and afterward upscaled back to the original size to recover details. Recent work in image rescaling formulates downscaling and upscaling as a unified task and learns a bijective mapping between HR and LR via invertible networks. However, in real-world applications (e.g., social media), most images are compressed for transmission. Lossy compression will lead to irreversible information loss on LR images, hence damaging the inverse upscaling procedure and degrading the reconstruction accuracy. In this paper, we propose the Self-Asymmetric Invertible Network (SAIN) for compression-aware image rescaling. To tackle the distribution shift, we first develop an end-to-end asymmetric framework with two separate bijective mappings for high-quality and compressed LR images, respectively. Then, based on empirical analysis of this framework, we model the distribution of the lost information (including downscaling and compression) using isotropic Gaussian mixtures and propose the Enhanced Invertible Block to derive high-quality/compressed LR images in one forward pass. Besides, we design a set of losses to regularize the learned LR images and enhance the invertibility. Extensive experiments demonstrate the consistent improvements of SAIN across various image rescaling datasets in terms of both quantitative and qualitative evaluation under standard image compression formats (i.e., JPEG and WebP). Code is available at https://github.com/yang-jin-hai/SAIN.
Robust Domain Adaptation for Machine Reading Comprehension

Most domain adaptation methods for machine reading comprehension (MRC) use a pre-trained question-answer (QA) construction model to generate pseudo QA pairs for MRC transfer. Such a process will inevitably introduce mismatched pairs (i.e., Noisy Correspondence) due to i) the unavailable QA pairs in target documents, and ii) the domain shift during applying the QA construction model to the target domain. Undoubtedly, the noisy correspondence will degenerate the performance of MRC, which however is neglected by existing works. To solve such an untouched problem, we propose to construct QA pairs by additionally using the dialogue related to the documents, as well as a new domain adaptation method for MRC. Specifically, we propose Robust Domain Adaptation for Machine Reading Comprehension (RMRC) method which consists of an answer extractor (AE), a question selector (QS), and an MRC model. Specifically, RMRC filters out the irrelevant answers by estimating the correlation to the document via the AE, and extracts the questions by fusing the candidate questions in multiple rounds of dialogue chats via the QS. With the extracted QA pairs, MRC is fine-tuned and provides the feedback to optimize the QS through a novel reinforced self-training method. Thanks to the optimization of the QS, our method will greatly alleviate the noisy correspondence problem caused by the domain shift. To the best of our knowledge, this could be the first study to reveal the influence of noisy correspondence in domain adaptation MRC models and show a feasible solution to achieve the robustness against the mismatched pairs. Extensive experiments on three datasets demonstrate the effectiveness of our method.