Reviewerly/exHarmony huggingface dataset:
Evaluating the Use of Lexical Cohesion in Arabic Textbook Published by Indonesian Ministry of Religious Affairs

This study evaluates the use of lexical cohesion in Arabic textbooks published by the Indonesian Ministry of Religious Affairs. This qualitative research applies systemic functional linguistics analysis to the Class XII Madrasah Aliyah Arabic textbook. Research data was taken from five reading materials using proficient free-involved listening techniques and analyzed using the framework of lexical cohesion theory. The research results show the use of lexical cohesion devices consisting of repetition (71.4%), synonymy (9.8%), antonymy (3.2%), hyponymy (8.1%), meronymy (6.5%), and collocation (1.0%). From the distribution and quality aspect, lexical cohesion could be more effective and efficient. Quantitatively, the large number of gaps in the lexical cohesion devices shows the ineffectiveness of using cohesion devices. Qualitatively, the uneven distribution of each cohesion device uncovers the ineffectiveness of all reading materials. This study recommends developing reading material in textbooks by using lexical cohesion evenly. Through this development, textbooks will become effective learning instruments to improve students' vocabulary, reading, and writing competency.
The Role of Public Management Information System in The Organization of Nagari Government III Koto Aur Malintang Government

A management information system is an absolute necessity in every organizational body. Having a management information system is very helpful in carrying out the work processes of the organization or agency itself so that in the end it can work effectively, efficiently and as expected. The purpose of this research is to determine the role and efforts made by nagari in implementing digital nagari, this is done to increase the capability of nagari apparatus in providing services to the community. This research uses a qualitative method where the results obtained are descriptive data written based on sources obtained from books, journals, articles and the Nagari website. From the research carried out, it was found that the results of the discussion were that the use of information and communication technology must also be followed by the nagari government because the nagari is the spearhead of services that directly relate to the community so that work becomes more effective, efficient and able to fulfill the principles of information disclosure that have been mandated by the Openness Law. Public Information Number 14 of 2008. Efforts that can be made by nagari are by creating an official nagari website and an online sipena application which is useful for providing information effectively and efficiently to the public.
Strategies and Tasks to Response to Cyber Terror in the Age of Intelligence Information

In the era of intelligent information, the technological development of the 4th industrial revolution is transforming all functions of the state, such as administration, economy, and industry, into cyberspace, which increases cyber threats such as hacking and crime in virtual space as well as positive functions. In recent years, cyber terrorism has been increasing in various types of threats, such as industrial property theft, system paralysis, cyber warfare, and virtual currency theft, due to the expansion of the attack surface due to the expansion of the digital domain. The forms of attack are becoming more sophisticated. Now, the response to cyber terrorism should not be limited to the technological domain of information and communication, such as vaccine and defense program development, recovery system construction, and cyber forensic have to build Under this recognition, this paper aims to respond to cyber terrorism with strategies and tasks. First, unification of cyber terrorism response system and strengthening information sharing among response organizations through the enactment of the 'Framework Act on Cyber terrorism' in terms of laws and institutions. Second, cyber security experts Third, raising cyber security awareness and strengthening practice Fourth, establishing a cyber security inspection system and backup system Fifth, suggesting an AI-dedicated department and enactment of AI-related laws to actively respond to new vulnerable factors caused by the development of artificial intelligence technology.
Sosialisasi Pengolahan Limbah Organik Rumah Tangga Melalui Budidaya Magot di Desa Pandeyan, Kecamatan Grogol, Kabupaten Sukoharjo

Organic waste is an environmental problem, especially in the villages of Pandeyan, Grogol, Sukoharjo, so there is a need to solve this problem through direct community activities. One solution is the socialization activity on organic waste processing through magot cultivation in Pandeyan village. With this socialization, it is hoped that the community will gain further understanding regarding magot cultivation as an effort to process household organic waste to make it more useful. The socialization activity was conducted at the Pandeyan village office on August 30, 2023. To measure the success of socializing organic waste processing through magot cultivation, an evaluation was carried out using a questionnaire method distributed to participants. From the questionnaire results, it was found that 88.9% of the total participants understood magot cultivation. Meanwhile, if you look at the potential of organic waste in each household participating in this socialization, it is possible to process it through magot cultivation. However, there is still a need for assistance and further understanding regarding magot cultivation because there is still a stigma that maggots have a maggot-like shape and are not suitable for cultivation.
The constitutionality of the amendment to Article 11 of the Assembly and Demonstration Act - an absolute ban on assemblies in the vicinity of the executive office of the President and former President's residence

Recently, some members of National Assembly proposed an amendment to Article 11 of the ‘Assembly And Demonstration Act’ in order to ban on assemblies in the vicinity of the Executive Office of the President and former President's residence This paper will examine the constitutionality of this proposal.&#x0D; Absolute no-assembly zones have historically been used by military regimes as a means to curb citizens' freedom of assembly. These zones are generally thought to be unduly restrictive of freedom of assembly and do not pass the proportionality test. In fact, the Constitutional Court of Korea has ruled several parts of Article 11 of the Assembly And Demonstration Act unconstitutional.&#x0D; Before examining the constitutionality of this amendment proposal, we must first define the concept of “Presidential residence” in Article 11(3) of the Assembly And Demonstration Act. Taking into account the dictionary meaning of the term, the structure of Article 11 of the Assembly And Demonstration Act, and the guarantee of freedom of assembly, the term “presidential residence” refers to the living quarters of the President and his family.&#x0D; The absolute prohibition of all assemblies in the vicinity of the President's residence is aimed at protecting the constitutional functions of the President, and the absolute prohibition of all assemblies in the vicinity of the residence of former presidents is aimed at ensuring the personal safety of former presidents and the tranquility of their residences. These legislative objectives are legitimate. However, the above provisions uniformly prohibit all assemblies despite the fact that there is no need to regulate small and peaceful assemblies. Also, the legislative objectives can be achieved through the Assembly and Demonstration Act, the Criminal Code, and the Presidential Security Act. Therefore, these provisions violate the right to freedom of assembly. The amendments should not be passed.
Estimation of Potential GDP in South Korea Considering Monetary Policy and Financial Conditions

The policies of central banks, financial conditions, and economic fluctuations are not entirely independent; nevertheless, the conventional method for estimating potential GDP has the convenience of considering supply or real factors independently, potentially leading to biases in estimating the GDP gap that represents economic fluctuations. In this paper, we estimate potential GDP using a multivariate filtering approach that incorporates monetary policy and financial conditions during the period from 1996 to 2011. According to the estimation results, the level of potential GDP during the years 2003 to 2005 experienced a significant decline, and the GDP gap was observed to have a positive value. Despite this, expansionary economic policies were predominantly implemented during that time, which can be attributed to the limitations of the conventional method for estimating potential GDP. The findings of this study hold implications for future monetary policy formulation.
Reestablishment of Security Criminal Law System for New Terrorism

In the global trend of actively responding to new terrorism, such as enacting anti-terrorism laws in the form of 'hostile criminal law' or revising existing laws, the Republic of Korea is also a legislative device to respond to terrorist crimes in 2016. On February 2, the 「Anti-Terrorism Act for National Protection and Public Safety」was enacted. However, as the current security and criminal law does not cover all actors of terrorist crime, the boundaries of the punishment targets are very ambiguous, and each position in the legal system is shaking.&#x0D; In the special security situation of the Republic of Korea, where the terrorist threats of international terrorist crime organizations and the terrorist threats of North Korea, a warring partner in a state of armistice, exist, the security and criminal law system of the Republic of Korea can effectively respond to all types of terrorist crimes. The ultimate purpose of this study is to clarify the targets and roles of the “Anti-Terrorism Act for Safety””Criminal Law”“National Security Act”.&#x0D; Therefore, this study examines the problems of the current security and criminal law system due to the limitations of the 「Anti-Terrorism Act for National Protection and Public Safety」. After judging whether criminal types can be punished by the existing「Criminal Law」, the limitations of「Criminal Law」were pointed out. Third, the types of terrorist crimes that could not be punished by the「Anti-Terrorism Act for National Protection and Public Safety」After determining whether punishment is possible under the「National Security Act」, it pointed out the limitations of the「National Security Act」.
Knowledge Management in the Digital Age: Harnessing Information and Innovation with Knowledge Management Systems

In the dynamic landscape of the digital age, effective knowledge management has become paramount for organizations aiming to harness information and drive innovation. This research paper delves into the intricate interplay between knowledge management, information leveraging, and innovation within the context of the digital era. Leveraging the power of bibliometric analysis, this study examines the trends, influential authors, key concepts, and research gaps in the field. Knowledge Management Systems (KMS) emerge as crucial tools, facilitating the storage, sharing, and creation of knowledge. By systematically analyzing scholarly literature, this research contributes to a comprehensive understanding of knowledge management's evolving role in the digital age, shedding light on its implications for both theory and practice.
Freespace Optical Interferometric Reconfigurable Complex Convolution Module

Here we demonstrate simultaneous optical complex amplitude and phase modulation in the Fourier domain by deploying SLMs in focal plane of thin lenses to realize a 4F-interferometer and discuss system fidelity and complex convolution performance.
Nonclassicality and Non-gaussianity of Quantum States Generated with Pulsed GSM Pump

This study investigates the nonclassical and non-gaussian features of twomode squeezed vacuum state and photon-subtracted squeezed vacuum state generated with a partially temporally coherent pump which could be useful for long-distance quantum communication.
Accelerating Scientific and Engineering Applications through Cloud-based GPU Computing

As the extensibility of GPU computing rapidly increases, we often find them useful for different applications in the field of science and engineering. Libraries written for engineer- ing tasks such as CULA (Cuda Linear Algebra), cuFFT-Cuda Fast Fourier Transforms, and cuBLAS library- Cuda Basic Linear Algebra Subprograms) have made it easier for programmers to achieve a significant performance increase when solving problems in the fields of engineering and math. In signal processing we can use the GPU to perform discrete Fourier transforms on time-domain signal strength to represent the data in the frequency domain. With the data in this format, we can calculate signal strength of various frequen- cies very efficiently, and further determine if a transmission on a particular frequency has taken place. Speedups in excess of 70 were achievable using a GPU-based implementation utilizing the cuFFT library over a CPU implementation utilizing the most performance optimized CPU-based FFT library, FFTW.
InChI Isotopologue and Isotopomer Specifications

Abstract This work presents a proposed extension to the International Union of Pure and Applied Chemistry (IUPAC) International Chemical Identifier (InChI) standard that allows the representation of isotopically-resolved chemical entities at varying levels of ambiguity in isotope location. This extension includes an improved interpretation of the current isotopic layer within the InChI standard and a new isotopologue layer specification for representing chemical intensities with ambiguous isotope localization. Both improvements support the unique isotopically-resolved chemical identification of features detected and measured in analytical instrumentation, specifically nuclear magnetic resonance and mass spectrometry. This new extension to the InChI standard would enable improved annotation of analytical datasets characterizing chemical entities, supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) guiding principles of data stewardship for chemical datasets, ultimately promoting Open Science in chemistry.
Automatic Contrast Generation from Contrastless Computed Tomography

The use of contrast-enhanced computed tomography (CTCA) for detection of coronary artery disease (CAD) exposes patients to the risks of iodine contrast-agents and excessive radiation, increases scanning time and healthcare costs. Deep learning generative models have the potential to artificially create a pseudo-enhanced image from non-contrast computed tomography (CT) scans.In this work, two specific models of generative adversarial networks (GANs) - the Pix2Pix-GAN and the Cycle-GAN – were tested with paired non-contrasted CT and CTCA scans from a private and public dataset. Furthermore, an exploratory analysis of the trade-off of using 2D and 3D inputs and architectures was performed. Using only the Structural Similarity Index Measure (SSIM) and the Peak Signal-to-Noise Ratio (PSNR), it could be concluded that the Pix2Pix-GAN using 2D data reached better results with 0.492 SSIM and 16.375 dB PSNR. However, visual analysis of the output shows significant blur in the generated images, which is not the case for the Cycle-GAN models. This behavior can be captured by the evaluation of the Fréchet Inception Distance (FID), that represents a fundamental performance metric that is usually not considered by related works in the literature.Clinical relevance— Contrast-enhanced computed tomography is the first line imaging modality to detect CAD resulting in unnecessary exposition to the risk of iodine contrast and radiation in particularly in young patients with no disease. This algorithm has the potential of being translated into clinical practice as a screening method for CAD in asymptomatic subjects or quick rule-out method of CAD in the acute setting or centres with no CTCA service. This strategy can eventually represent a reduction in the need for CTCA reducing its burden and associated costs.
A Knowledge-based and Data-driven Approach for Predicting Acute Kidney Injury in Patients with Heart Failure<sup>*</sup>

It has great potential to integrate medical knowledge and electronic health record data for diagnosis prediction. However, present studies only utilized information from knowledge graphs, omitting potentially significant global graph structural features. In this study, we proposed a knowledge and data integrating modeling approach to reconstruct patient electronic health record data with graph structure and use medical knowledge as internal information of patient data to build a risk prediction model for acute kidney injury in patients with heart failure based on graph neural networks. Experimental results based on the MIMIC III data showed that the method proposed was superior to other baseline models in predicting the risk of acute kidney injury in heart failure patients, with an accuracy of 0.725 and an F1 score of 0.755. This study provides a novel approach to the disease risk prediction models that integrates medical knowledge and data.
Identification of Offensive Language in Social Media Using Prompt Learning

Offensive language refers to the use of language in a manner that may offend or harm others who are within earshot or view in a public place. Given the importance of identifying such language in social media for promoting emotional well-being, we propose a prompt learning method and compare its performance with fine-tuning on two widely used datasets, HatEval and OffensEval. Experimental results demonstrate that prompt learning can achieve a performance improvement over fine-tuning in a fully supervised setting.
Document Understanding for Healthcare Referrals

Reliance on scanned documents and fax communication for healthcare referrals leads to high administrative costs and errors that may affect patient care. In this work we propose a hybrid model leveraging LayoutLMv3 along with domain-specific rules to identify key patient, physician, and exam-related entities in faxed referral documents. We explore some of the challenges in applying a document understanding model to referrals, which have formats varying by medical practice, and evaluate model performance using MUC-5 metrics to obtain appropriate metrics for the practical use case. Our analysis shows the addition of domain-specific rules to the transformer model yields greatly increased precision and F1 scores, suggesting a hybrid model trained on a curated dataset can increase efficiency in referral management.
Industrial Anomaly Detection using Vision Based Cross Transformers

Few-shot anomaly detection (FSAD) using registration-based techniques for the identification of aberration in image and video feeds has attracted a great lot of researchers in the field. Introduction of transformer with the siamese network has made metric-based learning promising in the identification of anomalies in image streams with only a few shots of comparative support sets. Inspired by how humans use their comparative analysis for identification of differences in images, a registration-based anomaly detection system is introduced leveraging "Fully Cross Transformer" as the backbone architecture with a Siamese network encoder for feature-level registration and finally, loss estimation using Gaussian distribution for achieving probabilistic representation of the normal class. The proposed method incorporates a cross-attention The mechanism for a two-branch-based FSAD model with different batch sizes. Multilevel interactions for feature aggregations are used in the model for obtaining low-level feature representations. Extensive experimentation on various datasets have been conducted with promising results including MVTec AD, Retinal OCT, and Brain MRI. The application for the model includes the identification of Industrial Manufacturing, Disease identification, Agriculture, Earth Resource Management, and other fields.
A Secured Blockchain Framework for Healthcare Data Management System

In the healthcare system, electronic medical records are very critical, and they must be authenticated and verified. During the medical check-up, a large amount of patient medical data is generated which includes reports related to blood, life-threatening diseases, and personal information such as credit card numbers and addresses. Any privacy breach in patient medical records will bring various risks. A simple blockchain (Ethereum) can be effective to validate and authenticate stored data by deploying an immutable ledger. However, the main challenge in the simple blockchain is that its data can be easily accessible. In this paper, the authors create a business network for healthcare using Hyperledger fabric, which ensures that data is only available to the concerned person and its access rights are granted and revoked by the concerned participant. Additionally, the authors tested different scenarios to access blockchain security and its benefits.
Gated Convolution and Stacked Self-Attention Encoder–Decoder-Based Model for Offline Handwritten Ethiopic Text Recognition

Offline handwritten text recognition (HTR) is a long-standing research project for a wide range of applications, including assisting visually impaired users, humans and robot interactions, and the automatic entry of business documents. However, due to variations in writing styles, visual similarities between different characters, overlap between characters, and source document noise, designing an accurate and flexible HTR system is challenging. The problem becomes serious when the algorithm has a low learning capacity and when the text used is complex and has a lot of characters in the writing system, such as Ethiopic script. In this paper, we propose a new model that recognizes offline handwritten Ethiopic text using a gated convolution and stacked self-attention encoder–decoder network. The proposed model has a feature extraction layer, an encoder layer, and a decoder layer. The feature extraction layer extracts high-dimensional invariant feature maps from the input handwritten image. Using the extracted feature maps, the encoder and decoder layers transcribe the corresponding text. For the training and testing of the proposed model, we prepare an offline handwritten Ethiopic text-line dataset (HETD) with 2800 samples and a handwritten Ethiopic word dataset (HEWD) with 10,540 samples obtained from 250 volunteers. The experiment results of the proposed model on HETD show a 9.17 and 13.11 Character Error Rate (CER) and Word Error Rate (WER), respectively. However, the model on HEWD shows an 8.22 and 9.17 CER and WER, respectively. These results and the prepared datasets will be used as a baseline for future research.