Reviewerly/exHarmony huggingface dataset:
Analyzing the Effects of Government Subsidies on Dual-Channel Fresh Food Supply Chains with the Implementation of Blockchain Traceability Technology

With the rapid development of the social economy, consumer preferences have evolved significantly. Nowadays, consumers place a higher emphasis on product quality and origin. The challenge is to ensure effective supervision of the entire food production process, from farm to consumers' tables, and to address traceability and accountability issues when food safety problems arise. The emergence of blockchain technology offers a promising solution. Blockchain's decentralization and traceability capabilities can effectively monitor food status information. However, given the cost of implementing blockchain technology and the varying degrees of sensitivity consumers have to product quality and price, the question arises: should supply chains have strong incentives to adopt blockchain technology? This paper focuses on the fresh agricultural product supply chain, encompassing both online and offline retailers, and employs the Stackberg game model to examine the impact of blockchain technology adoption under a government subsidy scenario. The research findings of this article indicate that the decision to introduce blockchain technology by online suppliers is influenced by the cost associated with each unit of fresh products. Furthermore, government subsidies prove advantageous primarily for online retailers.
Batch Normalization based Convolutional Block YOLOv3 Real Time Object Detection of Moving Images with Backdrop Adjustment

Moving object detection and counting the number of objects is a prominent challenge for researchers in various application fields. The most significant things that play a vital part in the creation and advancement of computer vision systems are anomaly detection and object recognition. The most important aspect of object recognition utilizing an image, video, or webcam is the prediction of objects with high efficiency and accuracy. This study seeks to solve the performance issue by combining a Deep Learning-based neural network method with the model You Only Look Once (YOLO), and OpenCV. Unlike previous Algorithms, YOLOv3 simply takes a whole image once and forecasts the class probabilities for the bounding boxes. To detect the items in the frame, final weights acquired by training the dataset on the Darknet are necessary. The performance of the YOLOv3 model mainly depends on the convolutional block layer creation. The main contribution of this research work is that the convolution block creation is iteratively examined with several layers to suggest the optimal model that portray higher accuracy. When using moving cameras, the visual background frequently seems to rotate, translate, and scale. As a result, modelling the backdrop or detecting moving objects based on optical flow is challenging. With this overview, Batch Normalization based Convolutional Block YOLOv3 (BNCBY) is proposed for detecting the objects and the number of objects from the real time image. We first estimate the background movements using a perspective transformation model, and then use a deep learning classifier built on hand labelled datasets to recognize moving product contenders there in foreground subtracked image. We discover spatial-temporal traits with each floating entity candidate through optical flow matching and then prune them based on their motion patterns relative to the backdrop. The proposed BNCBY model offer a techniquefor detecting moving objects that combines backdrop adjustment and deep learning. To determine the motion information in the picture, the background compensation approach is applied. The python scripting is used for implementation using keras packages. Python was coded on a Gtx Geforce Tesla V100 Nvidia graphics card server with a batch size of 64 and 80 training epochs. Implementation results exhibits that the proposed BNCBY model outperforms the existing models with the accuracy of 98% efficiency.
Pengembangan Media Pembelajaran Calistung Pada Anak Usia 4 – 6 Tahun Berbasis Android

Multimedia-based learning media is one of the essential tools in supporting the learning process, especially for early childhood education, such as children aged 4-6 years old. The issue with conventional teaching methods is the lack of visualization during material delivery, which hinders the effectiveness of the teaching and learning process. This leads to a decrease in children's interest and a sense of boredom during the learning process. The utilization of multimedia-based literacy learning media in the teaching and learning process can greatly assist in delivering educational materials in a more engaging and interactive manner. This is because multimedia-based learning media is equipped with features such as images, sound, text, and animations that enhance children's learning experiences in understanding the subject matter. Furthermore, multimedia-based learning media can also help reduce boredom levels among children during the teaching and learning process. This is because children are more likely to engage and stay focused on the learning process when they use interesting and interactive media. The development method of multimedia drills and practice used by the author in creating multimedia-based literacy learning media is highly effective. This method allows children to practice and review the subject matter repeatedly until they truly understand and master it. Overall, the utilization of multimedia-based learning media in literacy education can provide numerous benefits for children aged 4-6 years old. This media can enhance children's learning experiences in understanding educational materials, expedite the learning process, and help reduce boredom levels among children during the learning process. Therefore, it is essential for educators to leverage multimedia-based learning media as a tool to enhance the quality of education for early childhood learners.
Cellular Automata Inspired Multistable Origami Metamaterials for Mechanical Learning (Adv. Sci. 34/2023)

Mechanical Learning In article number 2305146, Zuolin Liu, Hongbin Fang, Jian Xu, and Kon-Well Wang propose an innovative approach for mechanical learning through multistable origami metamaterials. Inspired by cellular automata, the multistable transition sequences are served as computing resources in the framework of reservoir computing. Digit recognition and complex computation tasks are successfully implemented through experiments by a single actuator, revolutionizing the potential for intelligent materials in diverse applications from computational science to mechano-intelligence.
Automated Detection of Anomalous Events in Surveillance Video Scenes Using Deep Learning Techniques

The identification of anomalous events in surveillance camera situations is a critical issue in public safety and security. Traditional surveillance systems, reliant on human operators, are labor-intensive and prone to errors. To address this challenge, we propose a deep learning approach for the automated detection of unusual events in surveillance video scenes. Utilizing the remarkable performance of deep learning techniques in computer vision, we extract spatiotemporal information from video frames and classify them as normal or unusual using a combination of convolutional neural networks (CNNs) and long short-term memory (LSTM) networks. The objective of this project is to develop a deep learning approach that overcomes the limitations of traditional video surveillance systems by automatically detecting and classifying unusual events. The proposed approach combines CNNs to extract spatial features and LSTMs to model temporal dependencies between frames, effectively capturing both spatial and temporal characteristics of video scenes. Experiments were conducted on two public datasets for abnormal event detection, and the proposed approach outperformed state-of-the-art methods, achieving an accuracy of 80%. The significant implications of this approach for public safety and security lie in its ability to improve the accuracy and efficiency of video surveillance systems while reducing the workload of human operators. By automating the detection of unusual events, this deep learning approach can enhance overall surveillance capabilities and help prevent critical security issues.
Advancements in Fisheye Lens Image Stitching for Enhanced ADAS in Automated Vehicles

As Advanced Driver Assistance Systems (ADAS) become integral to the functionality of automated vehicles, the demand for comprehensive and distortion-free visual information is paramount. This survey paper meticulously examines the realm of image stitching utilizing fisheye lenses to cater to the unique needs of ADAS applications. Drawing insights from an extensive analysis of pertinent research papers, this survey delves into the diverse spectrum of techniques devised to enhance fisheye lens image stitching. Key focal points include the rectification of fisheye lens distortion, mitigation of image duplication artifacts, and the seamless synthesis of stitched images. Embracing advancements in computer vision, image processing, and the utilization of OpenGL-based rendering techniques, this survey encapsulates the evolving landscape of image stitching for ADAS in automated vehicles. By dissecting the challenges inherent to distortion correction and object duplication removal, this paper uncovers the progressive strategies employed to elevate the accuracy and visual fidelity of stitched images in the context of ADAS. With a comprehensive synthesis of existing research, this survey serves as an indispensable compass for researchers, engineers, and practitioners navigating the intricate domain of image stitching within the realm of automated vehicle system.
THE MAIN TRENDS IN HIGHER ART EDUCATION

The innovative progress in improving the professional training of future artists is based on increased attention to the competence-based approach to professional training. When planning the educational path of future choreographers, it is essential to consider the role and specifics of their practical training. An effective practice occupies a prominent place in this process. It would take into account the development trends of the general social and artistic spheres. Such practices should cultivate skills that meet the demands and requirements of the contemporary world. The creation of qualitative conditions for the creative self-realization of a future choreography teacher allows both to consolidate knowledge and skills and to form a model of effective future professional activity. This article aims to identify the key trends in the development of higher art education. The paper outlines the prerequisites and peculiarities of trend formation while considering the features of the modern artistic environment. The study also aims to highlight the most promising change directions in higher art education, assessing the most significant factors contributing to their emergence and development. During the research, analytical methods were employed to process information regarding applying new working methods in higher art education. Additionally, the authors used the methods such as induction, deduction, analysis, synthesis of information, system-structural and comparative approaches, abstraction, and idealization for studying and processing data. The research has identified the leading, most important trends in higher art education, as well as the scientists' viewpoints on the key promising areas of development of higher education institutions in the field of art.
Application of Gaussian distribution-based resampling technique on imbalanced food dataset

The imbalanced food dataset is one of the various imbalanced datasets that are commonly found in the real world. For imbalanced datasets, we usually want algorithms to identify minority class samples in the imbalanced datasets, but traditional classification algorithms often suffer from performance degradation, overfitting and other problems. To address these problems, we propose a method based on Gaussian distribution resampling combined with a machine learning classifier. The method generates new samples in the form of Gaussian distribution by metering minority class samples in the unbalanced dataset, and after selecting the appropriate anchor samples, a balanced dataset is obtained. Finally, the balanced dataset is classified by a classifier. By conducting experiments on two real imbalanced food datasets, our method outperformed the classification performance of mainstream resampling methods on both datasets, achieving the best AUC and G-mean values. The experimental results demonstrate that our method can effectively identify minority class samples in the imbalanced food dataset and has some application value in the food field.
Understand Your Service API

Whether you are creating or consuming an API, you must understand all its implications. If you are the consumer, it is up to you to understand what calling its various functions will do. If you are creating an API, you also have a responsibility to your consumers. I will not go into all the rules here but will touch on two very severe problems that I have personally experienced in my professional life.
Noise Reduction for a Virtual Grid Using a Generative Adversarial Network in Breast X-ray Images

In this study, we aimed to address the issue of noise amplification after scatter correction when using a virtual grid in breast X-ray images. To achieve this, we suggested an algorithm for estimating noise level and developed a noise reduction algorithm based on generative adversarial networks (GANs). Synthetic scatter in breast X-ray images were collected using Sizgraphy equipment and scatter correction was performed using dedicated software. After scatter correction, we determined the level of noise using noise-level function plots and trained a GAN using 42 noise combinations. Subsequently, we obtained the resulting images and quantitatively evaluated their quality by measuring the contrast-to-noise ratio (CNR), coefficient of variance (COV), and normalized noise–power spectrum (NNPS). The evaluation revealed an improvement in the CNR by approximately 2.80%, an enhancement in the COV by 12.50%, and an overall improvement in the NNPS across all frequency ranges. In conclusion, the application of our GAN-based noise reduction algorithm effectively reduced noise and demonstrated the acquisition of improved-quality breast X-ray images.
Technology-Enhanced Student Success Program Short Report

This paper presents an LLM-generated “Short Report'' on designing technology-enhanced first-year success programs for first-generation college students at large, public universities. We describe the three-phase process used to generate the report, and the process evolution as we came to understand the LLM’s tendencies, while also highlighting the opportunities and challenges of integrating LLMs in the scholarly practitioner discourse. We explore whether Generative AI can serve as a valuable tool for educational and scholarly contexts. The current iteration of Large Language Models (LLMs) exhibits several critical shortcomings for reliably producing an article fit for publication, including imprecise information, overt hallucinations, problematic oversights, and vague language that would typically render it unsuitable for rigorous academic discussion. However, Generative AI can present us with viable, impactful ideas for improving learning. It is incumbent on us as educators to recognize, evaluate, and develop those ideas for our campuses and communities.
Intelligent Detection Method for Wildlife Based on Deep Learning

Wildlife is an important part of natural ecosystems and protecting wildlife plays a crucial role in maintaining ecological balance. The wildlife detection method for images and videos based on deep learning can save a lot of labor costs and is of great significance and value for the monitoring and protection of wildlife. However, the complex and changing outdoor environment often leads to less than satisfactory detection results due to insufficient lighting, mutual occlusion, and blurriness. The TMS-YOLO (Takin, Monkey, and Snow Leopard-You Only Look Once) proposed in this paper is a modification of YOLOv7, specifically optimized for wildlife detection. It uses the designed O-ELAN (Optimized Efficient Layer Aggregation Networks) and O-SPPCSPC (Optimized Spatial Pyramid Pooling Combined with Cross Stage Partial Channel) modules and incorporates the CBAM (Convolutional Block Attention Module) to enhance its suitability for this task. In simple terms, O-ELAN can preserve a portion of the original features through residual structures when extracting image features, resulting in more background and animal features. However, O-ELAN may include more background information in the extracted features. Therefore, we use CBAM after the backbone to suppress background features and enhance animal features. Then, when fusing the features, we use O-SPPCSPC with fewer network layers to avoid overfitting. Comparative experiments were conducted on a self-built dataset and a Turkish wildlife dataset. The results demonstrated that the enhanced TMS-YOLO models outperformed YOLOv7 on both datasets. The mAP (mean Average Precision) of YOLOv7 on the two datasets was 90.5% and 94.6%, respectively. In contrast, the mAP of TMS-YOLO in the two datasets was 93.4% and 95%, respectively. These findings indicate that TMS-YOLO can achieve more accurate wildlife detection compared to YOLOv7.
Triage of Acute Strokes Using an EMS-driven Rapid Arterial Occlusion Evaluation (RACE) Protocol: a Longitudinal Study. (P2.3-044)

May 6, 2019April 9, 2019Free AccessTriage of Acute Strokes Using an EMS-driven Rapid Arterial Occlusion Evaluation (RACE) Protocol: a Longitudinal Study. (P2.3-044)Ehad Afreen, Alicia Castonguay, Syed Zaidi, Mouhammad Jumaa, Nurose Karim, Giana Dawod, Nicholas Henkel, Hisham Salahuddin, Julie Shawver, and Andrea KorsnackAuthors Info & AffiliationsApril 9, 2019 issue92 (15_supplement)https://doi.org/10.1212/WNL.92.15_supplement.P2.3-044 Letters to the Editor
Two-stage mammography classification model using explainable-AI for ROI detection

This study introduces an enhanced version of a two-stage modelling approach using artificial intelligence (AI) for breast cancer detection in mammography screening. Leveraging a large dataset of 2,863,175 mammograms from the Norwegian breast cancer screening program, the approach uses two convolutional neural networks. A key enhancement over the prior methodology is the application of the explainable-AI method Layered GradCam for identifying regions of interest (ROIs) within the mammograms. The second neural network subsequently classifies these ROIs for malignancy. Layered GradCam is also used to display identified cancers to the user. By the AUC criterion, our model performs well, 0.974 for screen-detected and 0.931 for all cancers (screen-detected and interval), compared to a commercial program; 0.959 and 0.918, respectively. Comparisons with the radiologist cores indicates that the model has equal performance with two radiologists, and superior performance to one, for the detection of all cancers (screening- and interval type). Our tests indicate that our model generalizes well for different breast centers, but so far only images from a single manufacturer have been tested.
Public Health perspectives on Cryptocurrency: The Good, The Bad and The Ugly

Digital currencies like bitcoin utilize encryption to secure financial transactions, verify their legitimacy, and limit supply. Because there is no central authority over it, like a government or bank, we say that it is decentralized. Instead, cryptocurrency transactions are recorded on a public digital ledger called a blockchain, which serves as a decentralized database that records all transactions made with the currency.(1) The use of cryptography ensures that the transactions are secure and anonymous, making it difficult to counterfeit or double-spend the currency. On online exchanges, cryptocurrencies can be purchased and traded, and they can also be used to purchase products and services online.(2) There are risks and hurdles associated with cryptocurrencies, including as their volatility, regulatory ambiguity, and security concerns, even though they have the potential to revolutionize the way we think about money and banking. As with any investment or financial decision, it is important to do your research and consult with experts before investing in cryptocurrencies. There are many different types of cryptocurrencies, with new ones being created regularly.(1) Here are some of the most common types:
Stochastic Collapsed Variational Inference for Structured Gaussian Process Regression Networks

Abstract This paper presents an efficient variational inference framework for a family of structured Gaussian process regression network (SGPRN) models. We incorporate auxiliary inducing variables in latent functions and jointly treat both the distributions of the inducing variables and hyper-parameters as variational parameters. Then we take advantage of the collapsed representation of the model and propose structured variational distributions, which enables the decomposability of a tractable variational lower bound and leads to stochastic optimization. Our inference approach is able to model data in which outputs do not share a common input set, and with a computational complexity independent of the size of the inputs and outputs to easily handle datasets with missing values. Finally, we illustrate our approach on both synthetic and real data.
PARTNER COLLABORATION PROGRAM TO ASSIST THE 74 HAFIZUN ALIM FOUNDATION IN CREATING MOCKUPS OF THE AL-QURAN PILLARS

This Partner Collaboration Program aims to assist the 74 Hafidzun Alim Foundation in creating mockups of the Al-Quran Pillars through the design stage of 2D and 3D architectural drawings to be applied visually. This mockup can be simulated as educational material for memorizing the Al Quran graphically using digital methods. The implementation method involves six (6) stages involving lecturers, students and partners, from initial consultation to monitoring and evaluation. This activity seeks to solve the problem of partners who cannot develop their findings into products more easily recognized and understood by the Muslim community in memorizing letter numbers, letter names and the number of verses in the Al Quran. The result of this activity is a mockup of the Graphic Al Quran Pillar Educational model, which can be digitally simulated. Y74HA has been able to operate a 3D mockup model of the Pillar of the Al Quran with a digital system and use it to broadcast the method of memorizing the Al Quran graphically so that Muslims can easily find out and remember the surah number, surah name and number of verses, by simply pressing the Surah button—the desired Qoran. The assistance provided by Budi Luhur University in creating working drawings and making mockups of Al-Quran Pillar models for partners has succeeded in developing a new business practice plan for Y74HA, namely a method to create digital graphic Al-Qurans, as a foundation business unit.
A survey of target detection algorithms in deep learning

The application of deep learning techniques significantly improves the performance of target detection algorithms. The article provides a comprehensive discussion of target detection principles, methods, algorithms, and future trends. First, the principles of target detection algorithms are summarized, including tasks, evaluation indexes, and public datasets. The image-based target detection algorithms are divided into two levels: traditional target detection and deep learning-based target detection. A brief overview of the types and shortcomings of traditional target detection algorithms is provided. Deep learning-based target detection is divided into single-stage and dual-stage for specific description. It summarizes the mechanisms, advantages, and limitations of each type of algorithm, conducts a comparative analysis, and suggests the future research direction of deep learning-based target detection.