Reviewerly/exHarmony huggingface dataset:
Predicting health insurance claim frauds using supervised machine learning technique

The healthcare sector is growing quickly, making it a complex system. At the same time, fraud in this sector is becoming a serious issue. Misuse of the medical insurance systems is one of the problems. The automated detection of healthcare scams uses data mining and machine learning approaches. In this work, an effort is made to provide a review of healthcare industry frauds and the methods for spotting them. A proposed solution to address issues in health insurance claims involves using a machine learning model that focuses on utilizing appropriate methods, identifying key data sources, and examining the properties of healthcare data. To gain insight into the patterns of the features, univariate and bivariate analysis are conducted on the data. Afterwards, proper data visualization is performed to determine which feature has the greatest impact. Based on the pre-processed data, a machine learning model is constructed and compared with four algorithms: Logistic Regression, Random Forest, Decision Tree Classifier, and Naive Bayes. The highest accuracy was achieved through the Decision Tree Classifier algorithm, with an accuracy of 97.03%.
Akuntansi Syariah Sebagai Sistem Informasi

As an information system, accounting refers to gathering and disseminating economic data about a company entity to numerous parties. The management of our economic and social system depends heavily on accounting, which is at the forefront of this field. Decisions made by people, governments, and other economic entities are based on how they utilise the resources that belong to a country. The financial component and financial reporting records of an entity are among the most crucial elements since they have a significant influence on the company's viability. There is no outside research involved in this study. Finding out how Islamic accounting functions as an information system is the aim of this study. The content analysis method is the analytical technique applied. Based on the study's findings, Islamic accounting is an attempt to transform modern accounting into a humanist and moral system. Islamic accounting information systems emphasize Islamic values in both the accounting process and the underlying information systems. These systems have a heavy moral burden.
Research on Data Mining Algorithm Based on Student Behavior Analysis

In this paper, according to the current rapidly accelerating information environment, the collected student behavior data is stored, and the database used for storage is MySQL, after which a student behavior system based on Python language is designed. The data visualization stage is mainly to import the technical package into the environment, and then use Echarts technology for a graphical display of the relationship between the data, to understand the student behavior and activity status, and to achieve the effect of assisting teachers in making teaching decisions.
Detecting and Classifying Darknet Traffic Using Deep Network Chains

The anonymity of the darknet makes it attractive to secure communication lines from censorship. The analysis, monitoring, and categorization of Internet network traffic are essential for detecting darknet traffic that can generate a comprehensive characterization of dangerous users and assist in tracing malicious activities and reducing cybercrime. Furthermore, classifying darknet traffic is essential for real-time applications such as the timely monitoring of malware before attacks occur. This paper presents a two-stage deep network chain for detecting and classifying darknet traffic. In the first stage, anonymized darknet traffic, including VPN and Tor traffic related to hidden services provided by darknets, is detected. In the second stage, traffic related to VPNs and Tor services is classified based on their respective applications. The methodology of this paper was verified on a benchmark dataset containing VPN and Tor traffic. It achieved an accuracy of 96.8% and 94.4% in the detection and classification stages, respectively. Optimization and parameter tuning were performed in both stages to achieve more accurate results, enabling practitioners to combat alleged malicious activities and further detect such activities after outbreaks. In the classification stage, it was observed that the misclassifications were due to the audio and video streaming commonly used in shared real-time protocols. However, in cases where it is desired to distinguish between such activities accurately, the presented deep chain classifier can accommodate additional classifiers. Furthermore, additional classifiers could be added to the chain to categorize specific activities of interest further.
A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

The COVID-19 virus is usually spread by small droplets when talking, coughing and sneezing, so maintaining physical distance between people is necessary to slow the spread of the virus. The World Health Organization (WHO) recommends maintaining a social distance of at least six feet. In this paper, we developed a real-time pedestrian social distance risk alert system for COVID-19, which monitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge, thus avoiding the problem of too close social distance between pedestrians in public places. We design a lightweight convolutional neural network architecture to detect the distance between people more accurately. In addition, due to the limitation of camera placement, the previous algorithm based on flat view is not applicable to the social distance calculation for cameras, so we designed and developed a perspective conversion module to reduce the image in the video to a bird's eye view, which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user. We selected images containing only person labels in the COCO2017 dataset to train our network model. The experimental results show that our network model achieves 82.3% detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall, Precision and mAP, proving the effectiveness of our system and the efficiency of our technology.
GraNDe: Efficient Near-Data Processing Architecture for Graph Neural Networks

Graph Neural Network (GNN) models have attracted attention, given their high accuracy in interpreting graph data. One of the primary building blocks of a GNN model is aggregation, which gathers and averages the feature vectors corresponding to the nodes adjacent to each node. Aggregation works by multiplying the adjacency and feature matrices. The size of both matrices exceeds the on-chip cache capacity for many realistic datasets, and the adjacency matrix is highly sparse. These characteristics lead to little data reuse, causing intensive main-memory accesses during the aggregation process. Thus, aggregation exhibits memory-intensive characteristics and dominates most of the total execution time. In this paper, we propose GraNDe, an NDP architecture that accelerates memory-intensive aggregation operations by locating NDP modules near DRAM datapath to exploit rank-level parallelism. GraNDe maximizes bandwidth utilization by separating the memory channel path with the buffer chip in between so that pre-/post-processing in the host processor and reduction in NDP modules operate simultaneously. By exploring the preferred data mappings of the operand matrices to DRAM ranks, we architect GraNDe to support adaptive matrix mapping that applies the optimal mapping for each layer depending on the dimension of the layer and the configuration of a memory system. We also propose adj-bundle broadcasting and re-tiling optimizations to reduce the transfer time for adjacency matrix data and to improve feature vector data reusability by exploiting tiling with consideration of adjacency between nodes. GraNDe achieves 3.01× and 1.69× on average, and up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$4.00\times$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$1.98\times$</tex-math></inline-formula> speedups of GCN aggregation over the baseline system and the state-of-the-art NDP architecture for GCN, respectively.
On background bias in deep metric learning

Deep Metric Learning trains a neural network to map input images to a lower-dimensional embedding space such that similar images are closer together than dissimilar images. When used for item retrieval, a query image is embedded using the trained model and the closest items from a database storing their respective embeddings are returned as the most similar items for the query. Especially in product retrieval, where a user searches for a certain product by taking a photo of it, the image background is usually not important and thus should not influence the embedding process. Ideally, the retrieval process always returns fitting items for the photographed object, regardless of the environment the photo was taken in. In this paper, we analyze the influence of the image background on Deep Metric Learning models by utilizing five common loss functions and three common datasets. We find that Deep Metric Learning networks are prone to so-called background bias, which can lead to a severe decrease in retrieval performance when changing the image background during inference. We also show that replacing the background of images during training with random background images alleviates this issue. Since we use an automatic background removal method to do this background replacement, no additional manual labeling work and model changes are required while inference time stays the same. Qualitative and quantitative analyses, for which we introduce a new evaluation metric, confirm that models trained with replaced backgrounds attend more to the main object in the image, benefiting item retrieval systems.
Tracking objects using QR codes and deep learning

Despite recent advances in deep learning, object detection and tracking still require considerable manual and computational effort. First, we need to collect and create a database of hundreds or thousands of images of the target objects. Next we must annotate or curate the images to indicate the presence and position of the target objects within those images. Finally, we must train a CNN (convolution neural network) model to detect and locate the target objects in new images. This training is usually computationally intensive, consists of thousands of epochs, and can take tens of hours for each target object. Even after the model training in completed, there is still a chance of failure if the real-time tracking and object detection phases lack sufficient accuracy, precision, and/or speed for many important applications. Here we present a system and approach which minimizes the computational expense of the various steps in the training and real-time tracking process outlined above of for applications in the development of mixed-reality science laboratory experiences by using non-intrusive object-encoding 2D QR codes that are mounted directly onto the surfaces of the lab tools to be tracked. This system can start detecting and tracking it immediately and eliminates the laborious process of acquiring and annotating a new training dataset for every new lab tool to be tracked.
LUN-BiSeNetV2: A lightweight unstructured network based on BiSeNetV2 for road scene segmentation

With the continuous introduction of automatic driving technology, the research of road scene segmentation algorithm in machine vision has become very important. In traditional methods, most researchers use machine learning methods to segment thresholds. However, the introduction of deep learning in recent years makes convolutional neural networks widely used in this field. Aiming at the problem that the traditional threshold segmentation method is difficult to effectively extract the threshold value of road image in multiple scenes and the serious problem of over-segmentation caused by deep neural network training data directly, this paper proposes a road scene segmentation method based on a lightweight unstructured network based on BiSeNetV2. The network contains backbone segmentation network and BiSeNetV2 network. The Mobilenetv2 network is used in the backbone network to replace the Xception feature extraction network in the decoder. In addition, grouping convolution is used to replace common convolution in Mobilenetv2 network. And it selects the batch specification layer to reduce the number of parameters, without affecting the accuracy and improving the efficiency of segmentation. At the same time, due to the relatively fixed distribution position of unstructured roads in the image, attention mechanism is introduced to process advanced semantic features, so as to improve the sensitivity and accuracy of the network. The BiSeNetV2 network enhances the dominant relationship between channel features by adding a compression excitation module based on channel attention mechanism after the detail branch, so as to perceive key areas and highlight local features. The lightweight feature pyramid attention mechanism is used to optimize semantic branches, improve the feature integration between contexts, extract high-level road semantic information more efficiently and retain spatial location information to the maximum extent. Finally, local semantic features and high-level semantic features are fused to improve the effect of unstructured road detection. The experiment is trained on the open data set. The results show that compared with other state-of-theart networks, the accuracy and real-time performance of proposed LUN-BiSeNetV2 in this paper are good, and the false segmentation and edge clarity are better. Compared with the classical algorithm, the average intersection is improved by 2.2% compared with mIoU, the average pixel accuracy is improved by 7.6%, and the frame rate is improved by 24.5%.
A novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition

To solve the problems of large number of similar Chinese characters, difficult feature extraction and inaccurate recognition, we propose a novel multilevel stacked SqueezeNet model for handwritten Chinese character recognition. First, we design a deep convolutional neural network model for feature grouping extraction and fusion. The multilevel stacked feature group extraction module is used to extract the deep abstract feature information of the image and carry out the fusion between the different feature information modules. Secondly, we use the designed down-sampling and channel amplification modules to reduce the feature dimension while preserving the important information of the image. The feature information is refined and condensed to solve the overlapping and redundant problem of feature information. Thirdly, inter-layer feature fusion algorithm and Softmax classification function constrained by L2 norm are used. We further compress the parameter clipping to avoid the loss of too much accuracy due to the clipping of important parameters. The dynamic network surgery algorithm is used to ensure that the important parameters of the error deletion are reassembled. Experimental results on public data show that the designed recognition model in this paper can effectively improve the recognition rate of handwritten Chinese characters.
Method for the automatic measurement of camera-calibration quality in a surround-view system

Over the last decade, the automotive industry has introduced advanced driving assistance systems (ADAS) and automated driving (AD) features into roads to reduce fatality rates. One of these ADAS is the surround-view system, which provides an orthographic view of the vehicle by using at least four fish-eye lens cameras embedded in it. Small bumps or temperature changes may modify these cameras' relative poses leading to some geometrical mismatches between views in the top-view projection plane. In addition, terrain irregularities may misalign the orthographic view with the ground plane surface. Both problems can be solved by reestimating the relative poses of the cameras with respect to a single common point in the vehicle. This procedure, also known as recalibration, is offline performed in technical garages, or by online calibration mechanisms on engine start. However, it is a slow and cumbersome process. Research to date studies how to optimally recalibrate these cameras in an online manner, neglecting the practical aspects of when this procedure should be undertaken. Therefore, depending on the functionalities for which the embedded cameras are required, a compromise between using out-of-calibration cameras and the consequences derived from the recalibration process must be considered. This would prevent reestimating the cameras’ relative poses in situations where misalignment between adjacent cameras may not be noticeable. For this reason, a novel approach that measures the degree of calibration between cameras embedded in a vehicle is proposed. This method extracts relevant features from the predefined regions of interest of each camera by using the histogram of oriented gradients (HOG) descriptor. Then, features that belong to adjacent cameras are compared by employing the cosine similarity metric. The proposed method is evaluated on the open-source AD research simulator CARLA providing detailed analysis to objectively highlight the usefulness of this method in studying the degree of calibration of a camera array in a surround-view system.
A Surface-Normal Based Neural Framework for Colonoscopy Reconstruction

Reconstructing a 3D surface from colonoscopy video is challenging due to illumination and reflectivity variation in the video frame that can cause defective shape predictions. Aiming to overcome this challenge, we utilize the characteristics of surface normal vectors and develop a two-step neural framework that significantly improves the colonoscopy reconstruction quality. The normal-based depth initialization network trained with self-supervised normal consistency loss provides depth map initialization to the normal-depth refinement module, which utilizes the relationship between illumination and surface normals to refine the frame-wise normal and depth predictions recursively. Our framework’s depth accuracy performance on phantom colonoscopy data demonstrates the value of exploiting the surface normals in colonoscopy reconstruction, especially on en face views. Due to its low depth error, the prediction result from our framework will require limited post-processing to be clinically applicable for real-time colonoscopy reconstruction.
SADM: Sequence-Aware Diffusion Model for Longitudinal Medical Image Generation

Human organs constantly undergo anatomical changes due to a complex mix of short-term (e.g., heartbeat) and long-term (e.g., aging) factors. Evidently, prior knowledge of these factors will be beneficial when modeling their future state, i.e., via image generation. However, most of the medical image generation tasks only rely on the input from a single image, thus ignoring the sequential dependency even when longitudinal data is available. Sequence-aware deep generative models, where model input is a sequence of ordered and timestamped images, are still underexplored in the medical imaging domain that is featured by several unique challenges: 1) Sequences with various lengths; 2) Missing data or frame, and 3) High dimensionality. To this end, we propose a sequence-aware diffusion model (SADM) for the generation of longitudinal medical images. Recently, diffusion models have shown promising results in high-fidelity image generation. Our method extends this new technique by introducing a sequence-aware transformer as the conditional module in a diffusion model. The novel design enables learning longitudinal dependency even with missing data during training and allows autoregressive generation of a sequence of images during inference. Our extensive experiments on 3D longitudinal medical images demonstrate the effectiveness of SADM compared with baselines and alternative methods. The code is available at https://github.com/ubc-tea/SADM- Longitudinal-Medical-Image-Generation .
Meningkatkan Kemampuan Berbicara Anak Usia Empat Sampai Lima Tahun Melalui Media Pop-Up Book

The background of this research is motivated regarding children's speaking which is still low. The number of children experience the difficulty in speaking due to the pronunciation of the letters and words which is unclear. The purpose of this study is to know the children's speaking ability, before, in process, and after using the pop-up book media in every cycle in group A at RA Widuri Asy-Syifa Cinanjung, Tanjungsari, Sumedang. The approach uses in this study is mixed-method design which is combining the quantitative and qualitative approach. The method that uses by the researcher is Classroom Action Research. This research consisted of two cycles and each cycle consisted of planning, implementing, observing and reflecting. The data collection technique of this research using observation, documentation and performance. The data analysis of this research using descriptive and statistics. The result of the study showed that children's speaking ability before using pop-up book media obtained an average value of 56 with less criteria. The process of implementing pop-up book media showed an increase, the teacher activity in cycle I is 75% and cycle II is 90%. The activity of the first cycle of children is 68% and the second cycle is 88%. Therefore, the children's speaking ability after implementing pop-up book media has increased, cycle I the average value is 67 and cycle II the average value is 85. Thus, the hypothesis proposed is accepted, therefore using pop-up book media can improve children's speaking ability
THE USE OF BLOG TO INCREASE STUDENTS’ WRITING SKILL

This research finding out whether the blog increases the students’ writing skill. In this study, researcher used in this quantitative research is a pre-experimental method. This kind of study allows the researcher to look at the effects of at least one independent variable and one dependent variable. In this research, the researcher wanted to see whether the treatment make a difference or no. Population The population of the research was the eleventh grade students of SMA Negeri 4 Jeneponto in the academic year of 2019/2020. Sample, the sample was selected by using cluster random sampling technique. The researcher chose one class as the sample. Research Instrument, the instrument used in this research was writing test. To test writing ability the researcher will ask the students to write three paragraphs. There will be two tests, namely pre-test &amp; post-test.
The challenges faced by the nurse of the intensive care unit in the treatment of patients with Covid-19: An integrative literature

Objective: to highlight the main challenges faced by nurses in the intensive care unit in the treatment of patients affected by covid-19. Method: integrative literature review, operationalized in the scientific electronic library online databases, database on nursing, sus collection and latin american and caribbean literature on health sciences. Results: through a search of the chosen descriptors, 14 articles were selected from a sample of 403. 3 main axes of analysis were categorized: challenges related to structural issues of the icu environment, environmental challenges related to the hospital environment and psychosocial challenges related to the nursing profession applied to the covid icu. Conclusion: there are structural and psychosocial factors that impact the nursing service in the icu, and these factors need to be mitigated to improve the quality of life and health of professionals, and consequently to improve health care.