Reviewerly/exHarmony huggingface dataset:
Utilization of GIS for Adaptation Pattern on Community of Drought Disaster in Kaliori District, Rembang Regency

Abstract The adaptation process carried out by individuals or communities in counfront of drought forms an adaptation pattern. This research focuses on adaptation to domestic and agricultural water needs. The drought disaster in Kaliori is a hydrological drought where there is a shortage of surface water and groundwater, areas affected by drought occur in 20 of 23 villages in Kaliori. The goals of this research is to determine the adaptations made by the community in facing drought disasters. This research using a mixed method of quantitative and qualitative with primary data collection methods are interviews, questionnaires, Focus Group Discussion (FGD), and utilize GIS processing. The results is the dominant adaptation patterns is (1) Buying tank water from other districts for people with middle-high economic class, (2) Buying conducting water for IDR 2,000 from the District Lasem for people with a lower economy, (3) Prioritizing need for water for daily needs and drinking water for livestock, (4) Selling or renting paddy fields for farmers who do not have sufficient capital to work on the fields, (5) Switching professions to sectors other than agriculture, such as turning into fishermen and porters in the coastal area of Kaliori District; (6) Coastal communities who work as salt farmers benefit from the drought disaster because it results in higher salt production; and (7) Lowland people who work as farmers adjust the types of crops from rice to sugar cane, corn, beans, chilies, cassava, and other that can withstand hot weather.
Participatory Mapping Framework for Smart Web-GIS Disaster Monitoring in Slawi Urban Area, Tegal Regency

Abstract Low community capacity followed by high vulnerability to disaster leads to an increased risk of disaster. The affected communities are an essential resource for managing disasters because of their experience. One of the steps that can be taken to reduce disaster risk in an area is to reduce its vulnerability and increase its capacity. In line with that, combining technology and community participation in the smart city concept could be a holistic solution. The use of technology for disaster monitoring generates faster and efficient disaster information that helps increase the community capacity in disaster risk management. This research aims to increase community participation in fulfilling the needs of disaster mitigation based on smart cities through GIS in Slawi, Tegal. This research used a quantitative descriptive method using Web-GIS framework for monitoring disaster by utilizing participatory mapping and Focus Group Discussion (FGD) to obtain data of the community needs in study area. The results obtained were: a) the verified disaster-prone area map; b) The combination of community participation and technology for evacuation plan; c) systems needed for emergency response; and d) connecting key partners for information and funding the disaster recovery. The implementation of the framework helps in boosting the dimension of governance, people, environment, and living of smart cities.
Early Alarm: Robust Event Analysis for Power Systems using 1-D Fully Convolutional Network

This work presents a novel deep learning model for early, accurate, and robust detection, recognition, and temporal localization of multi-type events in large-scale power systems. The proposed method develops a unified 1-D fully convolutional network (FCN) model that takes time series of raw frequency signals measured from a power system as input, extracts distinguishing features, and predicts at every temporal point in the time series if an event is happening and what the type of the event is. Compared to existing methods, the proposed model eliminates the necessity for hand-crafted feature extraction or complicated data pre-processing, can flexibly handle input signals of arbitrary length, and precisely infer the event occurrence time. Most importantly, the model is intentionally trained with incomplete patterns, such that it is more robust to partial features of an event which is common in real-world online recognition, resulting in early alarm for power system failures. Extensive experimental results demonstrate that the proposed method achieves superior performance to the state-of-the-art, and also shows strong robustness to noise and system oscillations.
SISTEM PAKAR MENDIAGNOSA PENYAKIT PERUT DENGAN METODE FORWARD CHAINING

At present there are still many people who do not know about the types of stomach diseases that exits. In general, people only understand 1 or 2 types of stomach disease that exist and underestimate the syptoms of stomach pain they experience. While an upset stomach if not immediately known and treated can be a very dangereous disease that can cause death for sufferers anyway. Therefore, it is very important for the public to know the beginning and existing stomach aliments. Therefore, the authors provide a solution by creating an Expert Systems to Diagnose Stomach Diseases that can help the community in diagnosing stomach ailments that they are experiencing. Experts system is a computer application that is intended to assist in making decisions or solving problems in a spesific field. All the knowledge needed in the expert system is an amalgamation of several experts made by doctors, all the accuarcy data can be trusted. This research used the forward chaining method to get conclusion from the syptoms of stomach disease experienced by the community.
Sub-Linear Time Sampling Approach for Large-Scale Data Visualization Using Reinforcement Learning

As the compute capabilities of the modern supercomputers continue to rise, domain scientists are able to run their simulations at very fine spatial and temporal resolutions. Compared to the compute speeds, the I/O bandwidth continues to lag by orders of magnitude. This necessitates that the analysis and data reduction are performed in situ while the simulation generated data is still at the supercomputer memory. Recently, intelligent data-driven sampling schemes have been proposed by visualization researchers. These sampling methods are scalable, in situ capable and able to identify the feature regions of the data. Although powerful, these sampling schemes need to traverse through the data sets at least twice, which can become problematic as the simulations start to touch the exascale capabilities. In this paper, we propose to use a reinforcement learning-based approach to devise a sub-linear time sampling algorithm. Using multi-arm bandits, we show that we can identify samples that are of similar or better quality compared to the existing methods while only touching a small fraction of the original data. We use multiple simulation data sets to show the efficacy of our proposed method.
CogniPoseAI: A Futuristic AI-Enhanced Personal Trainer

In the last several years, human posture estimation is one of the key areas of research in the field of computer Vision. This research employs pose estimation using deep learning and machine learning approaches to analyze performance and provide real-time feedback repetitions.The application of machine learning in the fitness industry could potentially aid judges in accurately monitoring the number of repetitions executed during weightlifting or CrossFit competitions. The software application that recognizes a person's exercise posture, counts repetitions and provides detailed recommendations to improve their form. This process uses Mediapipe to recognize human poses and analyze datasets and real-time video to make decisions and Count repetitions of a specific exercise.
Analysis of Discovering Fraud in Master Card Based on Bidirectional GRU and CNN Based Model

Detecting and confirming MasterCard fraud (also known as “master card fraud detection”) is a popular course topic amongst students due to the usefulness of data processing and machine learning strategies in combating cybercrime. The three main parts of the proposed method are preprocessing, feature extraction, and model training. Preprocessing techniques are used to mine crucial information for identifying MasterCard fraud. Only by using the most advanced and efficient algorithms, the future transactions can be predicted with any degree of certainty. By employing these techniques, the proposed approach may confirm a variety of facts, including the correctness of authentication and the regularity of dealings. To accomplish feature extraction, the proposed approach used the PCA and SKM algorithms. In order to train the models following feature extraction, the proposed approach uses CNN, BiGRU, and BiGRU-CNN. Both the CNN and the BiGRU models are outperformed by the proposed model. With an approximate 98.87 percent accuracy, the proposed method is superior to other methods such as CNN and BiGRU.
Ontology based Semantic Information Retrieval System using Data Ranking

Information retrieval is the process of searching through a database for material that meets a specific request. Due to the tremendous rise in popularity of knowledge discovery apps, end users are increasingly expected to craft sophisticated database search queries in order to access relevant data. Semantic information retrieval uses semantic analysis to return results that are more directly related to the user's original query. When this happens, ontology-based knowledge representation can be more efficient than other methods of representation like semantic networks and frames at facilitating semantic retrieval. Users in this category are required to understand not just the semantic connections between data as well as the structural complexity of complex databases. To get around these problems, Researchers have been focusing on enhancing the relation between data and search requests to provide outcomes that are more in line with users' research objectives to avoid these issues. As a result of this, ontologies are being used more and more for knowledge representation and interactive query creation. Considerations for ontology modelling, processing, and the translation of ontological knowledge into database search queries are all addressed in suggested ontology-based information retrieval strategy. Our findings show that the algorithm achieves state-of-the-art performance on a variety of measures of semantic information retrieval efficacy, including precision, recall, and f-measure. It has been shown through experiments that the algorithm speeds up the process of semantic information retrieval and enhances the system's capacity for expressing knowledge.
DocumentQA-Leveraging LayoutLMv3 for Next-Level Question Answering

With the increasing prevalence of digital documents in various domains, the demand for efficient and accurate question-answering (QA) systems has grown significantly. Traditional QA models primarily focus on text-based content, but the complex structure and visual elements present in documents pose unique challenges. This project explores the integration of LayoutLMv3, an advanced language and vision model, to elevate document question answering to the next level. The key role of LayoutLMv3 in document question answering (QA) lies in its ability to handle complex documents that contain not only textual information but also intricate visual elements like tables, images, and graphs. Traditional QA models typically focus on processing plain text and may struggle to comprehend the spatial arrangement and structure of documents. However, documents often contain vital information in their layout, which, if overlooked, can lead to inaccurate or incomplete answers. LayoutLMv3 overcomes this limitation by integrating both language and vision components into a single model. Through comprehensive tests on a variety of document datasets, this study shows the efficiency of DocumentQA. This study also analyzes the LayoutLMv3's interpretability and how it affects the way questions are answered. Through visualization techniques, various insights are gained on how the model analyses the document's layout, text, and interactions to generate accurate answers. The implications of this research extend beyond document QA, as LayoutLMv3's unique fusion of language and vision has broader applications in information retrieval, natural language processing, and document understanding tasks. The performance of LayoutLMv3 over LayoutLmv2 is also compared in this work showing efficient results.
Age Estimation from Facial Images Using the YOLO Algorithm

A computer vision task called “age estimation based on facial images” seeks to estimate a person's age by examining their facial features. It has drawn a lot of interest because of its possible applications in numerous industries. In this research work, a novel approach based on YOLO Algorithm is proposed to solve age estimation. To implement age estimation using YOLO, the algorithm needs to be trained on a dataset that contains annotated images with corresponding age labels. During training, the YOLO network learns to detect facial features and extract relevant information related to age, such as wrinkles, skin texture, and facial expressions. Once trained, the YOLO algorithm can be used to estimate the age of individuals in new images. It analyzes the image and applies regression techniques to predict the age range of each detected face. The output can be a set of predefined age groups or a continuous numerical value representing the estimated age. The trained model is then tested against a validation dataset, and measures like mean absolute error and mean squared error are used to gauge how well it performed. The model can be used to determine the age of fresh facial photos after it has been validated. Experiments are conducted on the app a-real dataset and results are found to be better than current state of art techniques.
An Innovative Method for Snatch Theft Detection Using Convolutional LSTM-Auto Encoder Approach

Urban areas frequently experience snatch stealing, a severe threat to public safety. It entails forcibly seizing a victim's personal items, including purses or cell phones, before scurrying away from the scene. Due to how quickly they happen, it is difficult to identify snatch theft instances in real-time. The systems now in use to identify the snatching occurrences mainly rely on human participation, which may result in lengthy delays and mistakes. Consequently, there is a need for an automated method that can quickly and effectively identify these situations. Preprocessing, segmentation, feature extraction, and model training are all used in the suggested method. Gaussian Model is utilized for preprocessing. Canny edge is utilized for segmentation. LSTM-AE is used to train the model. Two further models, LSTM and AE, are contrasted with the suggested method. When compared to two existing approaches, the proposed strategy performs exceptionally well.
Hardware Implementation of Block Ciphers – A Case Study on Encrypted Image Transfer Over Universal Asynchronous Receiver Transmitter

Communication protocols such as Universal Asynchronous Receiver/Transmitter (UART), Serial Peripheral Interface (SPI), Inter-Integrated Circuits (I2C), and Computer Area Networks (CAN) exist to facilitate communication among devices. This work studies a comparison of the additional overhead of offering Advanced Encryption Standard (AES)-128 and PRESENT-64 algorithms with reconfigurable UART on the transmitter and receiver side to offer data encryption. Advanced Encryption Standard (AES) in the basic loop architecture and PRESENT in the fully unrolled architecture for their implementation in the proposed study of secured data transfer over the UART channel. AES-128 consumes almost three times more clock cycles to encrypt and decrypt than PRESENT-64. Considering that AES-128 takes only double the input size of PRESENT-64 and requires thrice the number of clock cycles, AES-128 is more time-consuming. Xilinx Field Programmable Gate Array (FPGA) implementation results suggest that PRESENT-64 consumes slightly more onboard resources than AES-128 since the proposed work employs an unrolled PRESENT architecture. The complete architecture is specified using Verilog HDL and implemented in Xilinx families, namely Artix7, Spartan6, Virtex6, and Virtex7 series of FPGAs.
Comparative Analysis of Techniques for Efficient Resource Utilization in Cloud Environments Through VM Placement Optimization

This study focuses on evaluating and comparing different VM placement techniques in a simulated cloud environment. The performance metrics, including resource utilization, load balancing, number of servers used, average response time, fault tolerance, and storage capacity, are analyzed for each technique. The results indicate that the Supervised technique achieves the highest resource utilization, load balancing, and storage capacity, along with the lowest average response time. The Bin Packing technique demonstrates efficient server utilization and medium fault tolerance. The Load-Aware and Genetic techniques exhibit a trade-off between resource utilization, load balancing, and fault tolerance. These findings provide valuable insights into selecting appropriate VM placement techniques based on specific requirements.
Data Ingestion - Cloud based Ingestion Analysis using NiFi

Data Ingestion has been an integral part of Data Analysis. Bringing the data from various heterogeneous sources to one common place and ensuring the data is captured in the appropriate format is the key for performing any Big data task. Data ingestion is performed using multiple frameworks across the industry and they all have their own set of benefits and drawbacks. Apache NiFi is one popular ingestion framework which is used widely and does Ingestion effectively. Ingestion is performed on various sources and the data is generally stored in clusters or cloud storage. In this paper, we have done the File Data Ingestion using the NiFi framework on a local machine and then on two cloud-based platforms, namely Google Cloud Platform (GCP) and Amazon Web Services (AWS). The objective is to understand the latency and performance of the NiFi tool on Cloud-based Ingestion and provide a comparative study against the typical Data Ingestion. The entire setup was done on a local machine and two corresponding cloud platforms namely GCP and AWS. The findings from the comparative analysis have been compiled in a tabular format and graphs are created for easy reference. The paper places emphasis on the significance of NiFi's data ingestion performance on Cloud Platform and attempts to present it as a major activity on the data ingestion platform for Cloud Ingestion Solution.
Predicting Weather Forecast Uncertainty based on Large Ensemble of Deep Learning Approach

Weather forecasting is fraught with peril. Accurate predictions are now possible thanks to the (NWP) models' rapid development over the past few decades. However, the surroundings are absolutely disorganized. Many people, therefore, reject making use of value estimations unless an associated uncertainty estimate is also offered. Currently, the only option to generate a confidence estimate for individual forecasts is to create an ensemble of numerical weather simulations, which is computationally pretty expensive. This study explores the possibility that machine learning approaches can provide an alternate approach to estimating the degree to which future weather predictions may deviate from the baseline. Preprocessing, feature selection, and model training make up the first three steps of the suggested methodology. Data preprocessing includes all cleaning, integration, selection, transformation, and reduction processes. To choose features, they employ autocorrelation, partial autocorrelation, and PCA. Following feature selection, the models are subsequently trained using CNN-GRU. The two most popular alternatives, GRU and CNN, are outperformed by the suggested strategy.
Unveiling Anomalies in Surveillance Videos Through Various Transfer Learning Models

Video anomaly detection touches upon the process of automatically identifying abnormal events or behaviors in video streams. It plays a crucial role in various domains, including security, surveillance, and safety monitoring. Traditional methods of video analysis often rely on manual inspection, which often requires a significant amount of time and is susceptible to human error. The concept of video anomaly detection aims to overcome these limitations by leveraging advanced technologies such as computer vision and machine learning. This research study performs a relative analysis on the models of deep learning such as VGG16, Resnet50, VGG19, DenseNet121 to deal with video anomaly detection. This study uses subsets of various classes of UCF crime datasets. The UCF Crime Dataset contains 1903 video clips captured from real-world surveillance cameras. Results show Densenet-121 with an ROC-AUC score of 0.85 performs better when vis-รก-vis the other models used in this study.
Advancing Twitter Sentiment Analysis: An Ensemble Approach with Transformer-XL, RoBERTa, and XGBoost

The emergence of social media has made it crucial to analyze and comprehend users' sentiments, especially on platforms like Twitter, which provide real-time data for sentiment analysis. This study proposes an innovative ensemble classifier model incorporating the latest advancements in Natural Language Processing (NLP) and machine learning technologies, such as Transformer-XL, RoBERTa, and XGBoost, for Twitter sentiment analysis. Transformer-XL is a state-of-the-art language model that can capture long-range dependencies in text data, making it ideal for handling sequential data like tweets. RoBERTa, on the other hand, is a variant of BERT that leverages a larger dataset and training time, resulting in improved performance in understanding the context and meaning of words in tweets. In addition to these powerful language models, XGBoost, an efficient gradient-boosting framework is integrated, into the ensemble. XGBoost is renowned for its ability to handle complex interactions between features and has been successfully applied in various machine learning tasks. This study leverage a diverse and comprehensive dataset extending beyond COVID-19 pandemic-related tweets to train the ensemble classifier model. It ensures the Transformer-XL-RoBERTa-XGBoost Ensemble model's versatility and generalizability for sentiment analysis across different contexts by including tweets from various domains and topics. Proposed experimental results demonstrates that the proposed ensemble classifier model exceeds individual classifiers with accuracy, precision, recall, and F1 score. Incorporating advanced NLP techniques and the power of XGBoost enables the ensemble model to achieve higher accuracy and better performance in sentiment analysis on Twitter data. By harnessing the potential of cutting-edge technologies, the study gains valuable insights into user sentiments, which can benefit various applications, including market analysis, brand perception monitoring, and social trend tracking.