Reviewerly/exHarmony huggingface dataset:
Potensi Terjadinya Banjir Rob Akibat Penurunan Muka Tanah dan Kenaikan Muka Air Laut di Kabupaten Badung, Bali

&#x0D; &#x0D; &#x0D; &#x0D; Badung Regency is one of the tourist areas crowded with visitors, so that development continues to be developed as a support for tourist areas, one of which is a hotel. Hotel construction can cause building loads in a certain period; it can cause land subsidence. Land subsidence that co-occurs with sea-level rise can potentially be a tidal flood in the future. This study aimed to determine the rate of land subsidence in Badung Regency and the potential for tidal flooding due to land subsidence and sea-level rise in Badung Regency. The method used to calculate the rate of land subsidence is Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR), which utilizes radar satellite imagery data Sentinel-1a IW level 1.1 images and Sentinel-1b IW level 1.1 images. The image data used in the SBAS-DInSAR method consists of 166 image data and Digital Elevation Model (DEM) data with a spatial resolution of 30 m which is used for the topographic phase removal phase. Meanwhile, land subsidence data is used to determine the potential for tidal flooding, and it is predicted using regression; then, an overlay is carried out with the highest tide data along with the value of sea-level rise every year. To facilitate the observation of land subsidence, the daily image data obtained are then averaged into monthly data in each village. The fastest land subsidence rate from 2014 to 2020 occurred in Tuban Village. The prediction of the most significant tidal flood in 2030 to 2040 will occur in Tuban Village due to the influence of rapid land subsidence in the village.&#x0D; &#x0D; &#x0D; &#x0D;
E-Knowledge Module Development with Sprint Method in Dr. Ramelan Navy Hospital

Background: Hospitals need to manage document systems well. Dr Ramelan Navy hospital only 15% of the training materials have been documented. Knowledge of basic infection control programs only reached 57.2% and effective communication reached 56%. Objective: The purpose of this study is to analyze the factors causing the documentation of training materials under the standard, to determine solutions to improve the documentation of training materials to reach the standard and to develop an activity plan in overcoming the problem of documentation of training materials that have not reached the standard. Methods: This study uses the analytical observational method. The method used is the Problem Solving Cycle (PSC) approach. Data obtained through interviews, observations, and documentation in several departments and work units, then processed through a number of stages in overcoming the problem of documentation of training materials. Problems are solved by using a problem solving cycle approach with five stages of problem analysis, strategy design, development strategy, implementation, monitoring, evaluation and feedback. Solution of problems with the development of knowledge modules in hospital management information system. Results: The results at the strategy development stage of the development of the module named E-Knowledge was developed using the Sprint method with five stages of map, sketch, divide, prototype and test. Conclusion: E Knowledge increase knowledge and improve personnel performance.
Pemetaan Perubahan Penggunaan Lahan Pesisir Bali Tenggara dengan Menggunakan Citra Landsat

Bali is an island that has abundant cultural customs; this causes the island Bali to become a favorite tourist destination in Indonesia. Tourism development in Bali is accused of being the cause of the destruction of the agricultural sector due to the large number of agricultural land that has been converted into built-up land. Southeast Bali is one of the areas experiencing land changes in its coastal areas; this can be seen in the reduction in agricultural land in the area. This research uses the remote sensing method to conduct research in the Southeast Bali region. The remote sensing method is effective because it can cover a broad research area, and data can be accessed free of charge by the United States Geological Survey. The images used in this study are Landsat 5 and 8 satellite images. The method used in this study is the maximum likelihood method, which is carried out to identify and classify land use classes. The results show that the building class increases dominant land use from 1995 to 2021, then the most significant land-use change is found in the vegetation area. The results obtained in this study indicate that the accuracy of Landsat 5 and 8 is still quite good. This can be seen from the overall accuracy, which is 92%.
Unmasking Deepfakes by Fusing Rich Features from Two-Stream CNN Model

In contrast to the traditional object detection methods, image manipulation detection focuses on tampering artifacts instead of image content, indicating that more depth features must be learned to detect image manipulation. Deepfakes are one of these techniques that have appeared in recent times and need to learn a lot of the richer features to be detected. Deepfakes are a harmful application that affects all segments of society. It is meant to change the person's face and replace it with another person using deep learning techniques. In this paper, we contribute to finding a solution to detect the fakes. A new two-stream CNN model-based deep learning is developed, where two streams are combined, exploiting the fusion layer. Following the fusion layer, the data is classified using the classification layer. The first stream is a semantic stream to extract specific features from the RGB image input to identify manipulation artifacts such as blurring variation, the boundary of the face mask, and lighting difference. The second stream is a texture stream that exploits the texture features extracted from a Gabor bank filters layer. The proposed strategy significantly outperformed the previous methods that were in use. The measured performance metrics have an accuracy of more than 99.5%.
A Novel Image Inpainting Technique Based on Isotropic Diffusion

Using the isotropic diffusion model as a foundation, a novel method for image inpainting was suggested. To restore various missing areas of diverse natural images, a modified version of the original isotropic model is used. When employing the original isotropic model, the results of the suggested technique are compared to those obtained results. Regarding building texture in the missing area and restoring significant missing sections, the results of the suggested model performed better than the results of the obtained isotropic model. The performance of the suggested model in comparison to the original isotropic model is examined using a number of picture quality measures, including MSE, PSNR, and SSIM. In comparison to the widely used isotropic model, the improved model performs better and provides better measures of quality assessment for a greater number of natural photos.
OBSTACLES TO DISTANCE EDUCATION DURING THE CORONA EPIDEMIC FROM THE POINT OF VIEW OF TEACHERS OF THE FIRST EPISODE IN THE SULTANATE OF OMAN

The research aimed to reveal the obstacles to distance education during the Corona epidemic from the point of view of the teachers of the first episode in the Sultanate of Oman, and to reveal the differences in the sample responses according to the years of experience, and the number of training courses in distance learning, The research sample consisted of (198) A teacher. To achieve the objectives of the research, the two researchers adopted the descriptive analytical approach. The results showed a high level of obstacles facing distance education during the Corona epidemic from the point of view of the first episode teachers. The research recommended the need to intensify training courses for teachers in distance education, and to address the obstacles they face.
Improving Vietnamese Intellectual Property Law in the Context of Developing Artificial Intelligence

The Fourth Industrial Revolution has had a strong impact not only on the natural sciences but also on the social sciences, including the legal sciences. The need to harmonize legislation and new technology requires analyzing the legal aspects of new technology as soon as possible and developing regulatory frameworks in a way that responds to development needs of new technologies. The article analyzes and evaluates the impacts of artificial intelligence technologies on the current legal-technical mechanisms for intellectual property rights protection in general and in Vietnam in particular, such as patent protection for algorithms in artificial intelligence, and copyright protection for objects created by artificial intelligence. Thereby, the author assesses the adaptation of Vietnam's current intellectual property law and makes recommendations to improve the law.
Investigating the Effect of Mother Tongue on the Iraqi Undergraduates’ Use of English Segmentals and Syllables

This study investigates the effect of mother tongue on the Iraqi undergraduates’ use of English segmentals and Syllables. English segmentals and Syllables are two basic topics that are studied under the domains of phonetics and phonology. Those Iraqi undergraduates consist of Iraqi EFL learners (the two terms are used interchangeably in this study). English segmentals and Syllables involve consonants, vowels, diphthongs, consonant clusters and vowel clusters. The problem of the study is that EFL Iraqi students have no capacity to recognize English consonants and syllables. However, the following questions are focused upon in this study:1) What are the English consonants and vowels which are not recognized by Iraqi undergraduates under the effect of their mother tongue?2) What are the types of consonant and vowel clusters which are not recognized by Iraqi undergraduates under the effect of their mother tongue?The aims of the study include investigating the effect of learners’ mother tongue on their recognition of English segmentals and syllables, knowing whether Iraqi learners at the university level face difficulties in the recognition of consonants and vowels, ensuring whether EFL Iraqi students have difficulties in the recognition of consonant clusters and vowel clusters, and determining whether the difficulties faced by Iraqi EFL learners are caused by the learners’ mother tongue.It is hypothesized that Iraqi EFL undergraduates do not recognize some English consonants, English vowels, diphthongs, some types of consonant clusters and vowel clusters. In addition, it is hypothesized that the incorrect answers of Iraqi learners’ recognition of consonants, simple vowels, diphthongs, consonant clusters and vowel clusters are more than the correct ones.To achieve its aims and verify its hypotheses, the study adopts a quantitative method of investigation. The study chooses a sample for the present study, designs the test containing items concerned with consonants, vowels, and consonant clusters and vowel clusters. It includes only a recognition question. After applying the test on 100 undergraduate students of fourth stage/Department of English/College of Education for Humanities/University of Thi-Qar, the study arrives at some conclusions, some of which include that most Iraqi EFL learners find difficulty in recognizing some consonants, they appeared to be efficient in recognizing some simple vowels and diphthongs, they have the capacity to distinguish only some consonants but they are unable to recognize other consonant clusters and they appeared to be also inefficient in recognizing all vowel clusters.
Retinex-Based Light Source Generation for Relighting up Night Photography

Abstract The lighting up of buildings is one of the entertainments that make a city more colorful, and photographer sometimes changes the lighting by using photo-editing applications. This paper proposes a method for automatically performing such an application that follows the Retinex theory. The Retinex theory indicates that the complex scenes caught by the human visual system are decomposed into illumination and reflectance components. Many Retinex-based applications take much cost into the decomposition, while we show that the decomposition accuracy is less dependent on the final output for the nightscape relighting. First, we propose an extracting method of a relighting saliency map using Retinex with edge-preserving filtering. Second, we propose a sampling method to specify the lighting area in the limited area. We show that relighting within the limited area can be represented without illumination and reflectance decomposition. We then show that this fact can be exploited to realize this application using a simple Retinex decomposition of the single-scale Retinex with fast edge-preserving filters to represent various lighting effects. Experimental results show that the proposed sampling method is successful in keeping the illuminated points in bright locations and equally spaced.Also, the proposed various diffusion method can enhance night skyline photographs with various expressions. Our code and dataset are available at https://github.com/norishigefukushima/RelightingUpNightPhotography.
Research Method for Assessing the Potential of Solar Energy Source: Case Study in Thanh Hoa Province

&#x0D; This research uses the GHI (Global Horizontal Irradiation Index) raster data source of solar radiation potential map developed by The World Bank Group and the land use map data, infrastructure in 2019 of Thanh Hoa province to calculate the solar radiation technical potential in Thanh Hoa province. In addition, GIS techniques are also applied to overlay thematic maps to determine the available solar energy development area according to the method of the Institute of Energy. The results show that the available solar energy development area is 54,621.15 ha, accounting for 5% of the province's natural area, the technical potential is 21,848.46 MWp. The well-developed regions are concentrated in the lowland and midland districts due to favorable conditions of land scale and infrastructure, less affected by natural disasters such as storms, floods and landslides. Regarding the method of determining technical potential, it is required to be more specific to identify available and exclusion areas with some types of land such as water surface, forest and paddy rice land.&#x0D;
Internet of Things with Deep Learning Enabled Fraud Detection in Surveillance Video Processing

Background: In the present digital era, fraud detection using surveillance video has become a mandatory tool to determine the occurrence of abnormal events in an automated way.Since the traditional visual inspection of surveillance videos for fraud detection is time-consuming and labour-intensive, intelligent fraud detection approaches based on Deep Learning (DL) concepts have been presented in the literature.Methods: This paper designs a DL with optimal classification based on fraud detection in a video surveillance system, named the DLOC-FDVS technique.The proposed DLOC-FDVS technique aims to examine the surveillance videos for the existence of frauds (i.e., robbery) in the IoT environment.At the initial stage, IoT enables the data acquisition and frame conversion process to be carried out.For fraud detection, densely connected networks (DenseNet-169) feature extractor and optimal Long Short Term Memory (LSTM) classifiers are applied.Finally, the Grasshopper Optimization Algorithm (GOA) is utilised to alter the LSTM model's hyperparameters.It is frequently employed in a number of industrial settings and achieves suitable answers due to its ease of deployment and excellent precision.Findings: The DLOC-FDVS model is experimentally validated using a benchmark anomaly detection dataset from the Kaggle repository, which comprises 211 frames of normal video and 100 frames of fraud video.The experimental results show that the suggested model is an excellent fraud detection tool in the IoT context, achieving maximum precision, recall, F1score, and AUC of 96.56%, 96.56%, 96.56%, and 96.56% respectively.Novelty: The use of GOA for hyperparameter adjustment of the LSTM model for fraud detection demonstrates the work's uniqueness.As a result, the DLOC-FDVS model may be used to identify fraud in real-time surveillance recordings.
A Machine Learning Model for Estimation of Village Level Soil Nutrient Index

Objectives:To propose an innovative technique for designing an efficient and adaptive machine learning model using classifier assembly for estimating village level soil nutrient index using soil datasets.Methods: Freely available soil datasets were collected from the concerned authority of Govt. of India.These datasets were used by the proposed machine learning model designed with a classifier assembly of fifteen diverse classifiers for nutrient class identification.The performance of each classifier was evaluated in terms of five well-accepted standard metrics.The outputs of the best performing classifier were then used for estimation of village level nutrient index using modified Parker's method.Findings: The model was applied for nutrient class identification, and estimation of the nutrient index of different villages using freely available benchmarked Soil health Card datasets provided by the Govt. of India.The empirical results depicted that for nutrient class identification, this proposed machine learning model overperformed the other existing models in terms of average accuracy score.In the case of Copper, it provided the highest average accuracy of classification (0.949) and estimation accuracy of 95.48%.For Sulphur, an average classification accuracy of 0.891 and an estimation accuracy of 90.66% were achieved.Similarly, for Zinc, an average classification accuracy of 0.883 and an estimation accuracy of 89.63% were observed.Novelty: This study suggests a novel architecture of a machine learning model using classifier assembly to estimate the village level nutrient index with the highest possible accuracy, using freely available soil datasets.
Design and FPGA realization of Incremental Fuzzy controller for Stirred Tank Heater

Abstract In this paper, Field programmable gate array (FPGA) realization of fuzzy PI controller for Stirred Tank Heater system is presented. The commonly used PI controller in industry though it is simple for implementation lacks reliability when the dynamics of a system changes with time. Replacing PI controllers with AI systems can lead an effective control of a system even in the presence of model uncertainties. The digital implementation of AI systems in DSP or Microcontrollers have limitations of low computational accuracy and slow processing speed. Now adays, FPGAs are being largely used for digital implementation of algorithms because of their higher computational speed and accuracy. In this paper, fast and novel design approach for rapid prototyping of Fuzzy PI controller for Stirred Tank Heater is presented thoroughly. System Generator SIMULINK add on from VIVADO is used to generate Very High-Speed Integrated Circuit Hardware Description Language (VHDL) directly from MATLAB. The developed controller for stirred tank system is verified in Fixed point arithmetic using Xilinx Simulink blocks. The VHDL code is then generated, synthesized, implemented and made ready for physical implementation on Kintex-7 evaluation board.
PENERAPAN MODEL PEMBELAJARAN PROBLEM BASED INSTRUCTION (PBI) UNTUK MENINGKATKAN HASIL BELAJAR SISWA PADA PEMBELAJARAN BAHASA INDONESIA DI MTsN 1 ACEH UTARA

The problem raised in this CAR is whether the application of the Problem Based Instruction (PBI) Learning Model can improve student learning outcomes in Indonesian Language Learning Challenge Text Materials for Class IX/1 at MTsN 1 Aceh Utara. The problem solving used is in the form of classroom action research. The action was carried out in 2 cycles, each cycle consisting of 2 meetings, each meeting consisting of preliminary activities, core activities and closing activities. This article is equipped with theory and research methods along with the required instruments. The theory presented is related to Indonesian Language Subjects with Challenge Text Materials through the Problem Based Instruction (PBI) Learning Model. This article is equipped with observation instruments relating to the behavior of students in the learning process and observation instruments for teachers both observing the lesson plans and actions in the learning process. After taking action from 2 cycles consisting of 4 meetings, data were found which were the results of observer observations. After the data was discussed/analyzed, it was concluded that with the application of the Problem Based Instruction (PBI) learning model, student learning outcomes towards Indonesian Language Learning Challenge Text Materials in Class IX/1 MTsN 1 North Aceh showed an increase from cycle I to cycle II, where the results the final score is above the minimum standard (76%) which is 90.7%. Furthermore, the researcher submits the following recommendations; Teachers should prepare, design learning for students who have not reached the Minimum Completeness Criteria (KKM) using effective methods.
The future is waiting for us – The role of the permanent record in an enriched &amp; dynamic publishing ecosystem

This paper is based on a panel session on various aspects of the Version of Record (VOR) – the permanent record, held at the APE 2022 Conference. It argues that even with the current possibility of sharing versions and elements of the VOR, such as a preprint, an Author Accepted Manuscript or protocols, the VOR still remains valuable. The scholarly community should continue to protect the VOR and recognise it as an enabler of invention. This way, research can be communicated in more rapid, more open and more diverse ways, enabling readers to access a fuller picture of research.
Socio-inspired Evolutionary Algorithms: A Unified Framework and Survey

Abstract Evolutionary computation has gone through vast and diverse research endeavors in the past few decades. Although the initial inspiration came from Darwin’s ideas of biological evolution, the field has moved thereon to ideas from the collective intelligence of insects, birds, and fish, to name a few. A variety of algorithms have been proposed in the literature based on these ideas and have been shown to perform well in various applications. More recently, inspiration from human behavior and knowledge exchange and transformation has given rise to a new evolutionary computing paradigm. It is well recognized that human societies and problem-solving capabilities have evolved much faster than biological evolution. Many research endeavors have been reported in the literature inspired by diverse aspects of human societies with corresponding terminologies to describe the algorithm. These endeavors have resulted in a plethora of algorithms worded differently from each other, but the underlying mechanisms could be more or less similar, causing immense confusion for a new reader. This paper presents a generalized framework for these Socio-inspired Evolutionary Algorithms (SIEAs) or Socio-inspired Metaheuristic Algorithms. A survey of various SIEAs is provided to highlight the working of these algorithms on a common framework, their variations and improved versions proposed in the literature, and their applications in various fields of search and optimization. The algorithmic description of each SIEA enables a clearer understanding of the similarities and differences between these methodologies. Efforts have been made to provide an extensive list of references with due context. In that sense, this paper could become an excellent reference as a starting point for anyone interested in this fascinating field of research.