Reviewerly/exHarmony huggingface dataset:
Bioinspired Sensory Gating in the Artificial Vision System

When viewed as an enterprise, the healthcare system has important security and privacy requirements, such as protecting patient medical records from unauthorized access, tracking protected drugs, securely connecting to transportation such as ambulances, and conducting secure and intelligent e-health surveillance. Blockchain has introduced new ideas in medical data security and safety, and with the right security measures, it may eliminate the tension between sharing data and maintaining anonymity. In this study, the benefits of cloud computing are integrated with blockchain to offer a secrecy technique for blockchain and IoT. In addition to incorporating IoT and providing IoT solutions to blockchain nodes, this method also collects, analyzes, uses, and maintains identity authentication for health information. Interface and overcomes the insufficient computer power of particular blockchain chain routers to verify the data's feasibility and authenticity order to verify the feasibility and authenticity of the data. The simulation experiment shows that the suggested technique is effective. It may handle problems like high computer intricacy, data interchange, and privacy protection while maintaining and confirming the quality of medical data.
A Robust Recession Detective Analysis System using IoT Smart Sensor Devices

This study suggests a wearable gadget with an autonomous fall detector that can lower risks by identifying falls and notifying care takers right away. This research study combines a heart-rate sensor and an accelerometer to create a user-adaptive fall detection system based on cluster analysis. The suggested fall detector seeks to achieve high accuracy using a simple model under a variety of circumstances. Additionally, this research study tests the efficiency of the cluster-analysis-based anomaly identification as well as the performance improvement of combining a heart rate sensor and an accelerometer. This study also demonstrates the utility of the user-adaptive approach when using both acceleration and heart rate inputs. The system will alert the carer through GSM if the user's orientation data values become aberrant in any way. The system design takes into account a straightforward, inexpensive, and power-efficient design.
Artificial Neural Network using Image Processing for Digital Forensics Crime Scene Object Detection

Digital forensics science places a significant emphasis on the detection of objects as one of the most vital areas of study. Several industries and institutions may benefit from the object detection method, including those concerned with medical diagnostic scanning, traffic monitoring, airport security, law enforcement, and data rescue on a local and global scale. This study aims to detect weapons in video surveillance images by using various enhancement, segmentation, feature extraction, and classification methods by Artificial Neural Network to improve the detection accuracy. Yet, several mathematical and algorithmic models are computed to provide the appropriate approaches.
Efficient and Robust Robot Path Planning Based on Novel Wolf Pack Algorithm

In the context of the wave of intelligence, the effective identification of the environment and reasonable planning of the path are the major goals of visual robots. The quality of the path planning determines the efficiency of the completion of the rest of the tasks, and even the success of the task. In this study, the scheme is designed from 3 aspects: 1. the robot vision system to sense the information around is designed. This study applies the visual SLAM algorithm of the graph optimization framework with the hardware improvement to make the sensing performance better; 2. the novel gray wolf pack algorithm is designed. The algorithm has strong convergence performance, and has characteristics of the less parameters and easy implementation. Further, the parameters are optimized to make the performance better than the traditional ones; 3. the novel path planning system is implemented. In the experiment section, the testing on moving time and path length are both tested with the comparison of the traditional methods, it is shown that the proposed model has the better performance.
Purge Profanity: An AI System to Detect Profanity / Obscenity From Texts and Images

In many situations, such as workplaces and educational institutions where communication is supposed to be professional and respectful, profanity is frequently regarded as improper. However, policing obscenity has been harder as social media services like Discord have grown in popularity. This study suggests a system that may efficiently identify and eliminate profanity from both text-based communication and image-based communication in Discord to address this issue. The suggested system analyses information exchanged on Discord and flags any instances of swearing using a combination of natural language processing (NLP) and computer vision techniques. This method encourages better communication techniques in other online platforms where swearing might be a problem in addition to maintaining a safe and professional environment on Discord. By putting this approach in place, organisations and educational institutions may make sure that their online communication channels are devoid of objectionable material while also fostering a welcoming and inclusive online community.
Deep Learning based Sentiment Analysis on Images

Sentiment analysis is a powerful tool that has been gaining popularity among businesses in recent years. It involves analyzing text, speech, or images to understand the emotions or opinions conveyed by them. This process can provide valuable insights into customer preferences, behavior, and satisfaction levels. By leveraging sentiment analysis, businesses can gather customer feedback on their products and services, which can help them identify areas for improvement and develop stronger customer relationships. Furthermore, sentiment analysis can also be used to support emotional marketing campaigns, allowing businesses to create more targeted and personalized messaging that resonates with their audience. For example, by analyzing social media posts and comments, businesses can gain a better understanding of how their brand is perceived and adjust their marketing strategies accordingly. One popular method for this is transfer learning, where pre-trained models are used to analyze new datasets. Deep learning algorithms, such as convolutional neural networks (CNNs), have been particularly successful in achieving accurate results in a variety of applications, including image sentiment analysis. However, analyzing emotions conveyed through images is a complex task, and there is still much room for improvement. The Inception-v3 technique is a notable development in this field, as it can easily identify key parts of the body, such as the face, which is essential for accurately detecting emotions. The results of this model were compared to those of various other machine learning approaches, and the suggested model showed superior accuracy levels of up to 99.5%. This research demonstrates the potential of using deep learning algorithms and transfer learning methods to improve image sentiment analysis. For example, in the field of Healthcare, image sentiment analysis can be used to detect emotional expressions of patients, which can be valuable for assessing their mental and emotional state. In Sales and Marketing, it can be used to evaluate customers' emotional responses to different products or campaigns, allowing businesses to tailor their strategies accordingly. Overall, the use of image sentiment analysis can provide valuable insights to businesses across a wide range of industries.
Generating New Human Faces and Improving the Quality of Images Using Generative Adversarial Networks(GAN)

In recent years, deep learning models have gained popularity for producing realistic Images. Recent advancements in computer vision, particularly in deep generative models like GANs, have shown promise in synthesizing realistic images automatically. GANs use a competitive process involving two networks: a generative network and a discriminative network. The discriminative network determines whether an image is real or fake whereas the generative network generates artificial images. The generative network gains the ability to create more convincing images as training goes on in order to deceive the discriminative network. This research study intends to develop novel, high-resolution images of human faces by combining DCGAN (Deep Convolutional Generative Adversarial Network) with ESRGAN (Enhanced Super-Resolution Generative Adversarial Networks). DCGAN is a type of GAN that uses convolutional neural networks in both the generator and discriminator. The generator network learns to produce images from random noise, while the discriminator network learns to differentiate between real and fake images. Further, this study has used the CelebFaces Attributes Dataset (CelebA) to train the proposed DCGAN model, and the Structural Similarity Index (SSIM) to quantitatively evaluate the quality of the generated images. Additionally, ESRGAN is employed to improve the quality of the generated images. The obtained results reveal that combining DCGAN with ESRGAN produces high-quality human faces with clear details and improved resolution.
An Efficient Pre-Clusters Assessment Technique for Efficient Data Partitions

Massive information is being created over smartphones, IoT devices, and social media. Clusters are necessary for managing such unlabeled massive data for organizing the vast data into manageable data partitions. The survey is conducted on visual clustering methods for determining the number of clusters (also known as cluster tendency). These algorithms visually displayed the number of clusters as square-shaped dark blocks along the diagonal. Cluster tendency is depicted as a series of square, black blocks. The techniques, as mentioned earlier, perform admirably on small datasets. However, the clustering accuracy diminishes on large datasets and processing time increases. This paper proposed a system that improves upon the current state of the art in visual approaches to address this issue. Instead of using the traditional Euclidean distance to evaluate clustering, this work proposes a cosine metric for their visual algorithms. Experiments illustrating and contrasting the efficacy of proposed and existing visual clustering algorithms are carried out on benchmarked large datasets.
Research on Improved ViBe Algorithm for Moving Object Detection in Dynamic Background

Aiming at the problem that visual background extraction (vibe) algorithm is not capable of recognizing dynamic objects in camera motion state, an ORB-slam motion detection algorithm based on adaptive vibe is proposed. In this method, the RGB camera is used to collect information, and the motion speed of the camera is calculated by frame difference method, so that the background frame can be updated in time when the scene changes rapidly. After the model is initialized, the feature point matching algorithm is used to optimize the projection transformation to provide a more accurate correspondence between the current frame and the background frame. In the process of pixel matching, the dynamic threshold related to speed is introduced to replace the traditional fixed threshold. In the updating process, the update probability of the background model is set according to the speed to improve the robustness and adaptability of the algorithm. Experimental results show that the proposed method has good adaptability to dynamic algorithms.
Thangka Image Captioning Based on Semantic Concept Prompt and Multimodal Feature Optimization

Thangka images exhibit a high level of diversity and richness, and the existing deep learning-based image captioning methods generate poor accuracy and richness of Chinese captions for Thangka images. To address this issue, this paper proposes a Semantic Concept Prompt and Multimodal Feature Optimization network (SCAMF-Net). The Semantic Concept Prompt (SCP) module is introduced in the text encoding stage to obtain more semantic information about the Thangka by introducing contextual prompts, thus enhancing the richness of the description content. The Multimodal Feature Optimization (MFO) module is proposed to optimize the correlation between Thangka images and text. This module enhances the correlation between the image features and text features of the Thangka through the Captioner and Filter to more accurately describe the visual concept features of the Thangka. The experimental results demonstrate that our proposed method outperforms baseline models on the Thangka dataset in terms of BLEU-4, METEOR, ROUGE, CIDEr, and SPICE by 8.7%, 7.9%, 8.2%, 76.6%, and 5.7%, respectively. Furthermore, this method also exhibits superior performance compared to the state-of-the-art methods on the public MSCOCO dataset.
The Rise of Small-Scale Autonomous Distributed Power Systems

This chapter discusses the rise of small, self-run power systems. It emphasizes their freedom from traditional regulatory constraints, drawing parallels to evolutions in the information and communications industry. The chapter offers advice for easing regulations in the power sector, focusing on the importance of liberalizing distribution networks through tools like the Digital Grid Router (DGR). It promotes the idea of sharing power generated privately, discusses the implications of wheeling charge, and highlights the imbalanced structure of power transfer charges. The chapter suggests a transition toward a system where distributed power cells coexist and cooperate. It also notes the emerging role of local governments in this shift. Finally, it posits that the key to revitalizing regions may lie in innovative energy strategies.
Image camouflage and encryption scheme employing multimode fibers specklegram and polarization multiplexing technology

An image camouflage encryption scheme based on fiber specklegram and polarization multiplexing technology is proposed and demonstrated via simulation. The camouflaged original image is coupled into the multimode fiber (MMF) through different polarized light fields, and the specklegram caused by strong intermodal interference provides excellent chaos and randomness required for encryption. Since the optical fiber specklegram has a strong dependence on the configuration, transmission matrixes used as the key have high security and unpredictability. The results demonstrate the huge keyspace and excellent key sensitivity of the scheme. The ability to resist key disclosure and differential attack is analyzed. In addition, the scheme shows great security and reliability in several common noise attacks.
A Lightweight YOLOv5-Based Model with Feature Fusion and Dilation Convolution for Image Segmentation

Image segmentation has played an essential role in computer vision. The target detection model represented by YOLOv5 is widely used in image segmentation. However, YOLOv5 has performance bottlenecks such as object scale variation, object occlusion, computational volume, and speed when processing complex images. To solve these problems, an enhanced algorithm based on YOLOv5 is proposed. MobileViT is used as the backbone network of the YOLOv5 algorithm, and feature fusion and dilated convolution are added to the model. This method is validated on the COCO and PASCAL-VOC datasets. Experimental results show that it significantly reduces the processing time and achieves high segmentation quality with an accuracy of 95.32% on COCO and 96.02% on PASCAL-VOC. The improved model is 116 M, 52 M, and 76 M, smaller than U-Net, SegNet, and Mask R-CNN, respectively. This paper provides a new idea and method with which to solve the problems in the field of image segmentation, and the method has strong practicality and generalization value.
Automatic Medical Report Generation Based on Cross-View Attention and Visual-Semantic Long Short Term Memorys

Automatic medical report generation based on deep learning can improve the efficiency of diagnosis and reduce costs. Although several automatic report generation algorithms have been proposed, there are still two main challenges in generating more detailed and accurate diagnostic reports: using multi-view images reasonably and integrating visual and semantic features of key lesions effectively. To overcome these challenges, we propose a novel automatic report generation approach. We first propose the Cross-View Attention Module to process and strengthen the multi-perspective features of medical images, using mean square error loss to unify the learning effect of fusing single-view and multi-view images. Then, we design the module Medical Visual-Semantic Long Short Term Memorys to integrate and record the visual and semantic temporal information of each diagnostic sentence, which enhances the multi-modal features to generate more accurate diagnostic sentences. Applied to the open-source Indiana University X-ray dataset, our model achieved an average improvement of 0.8% over the state-of-the-art (SOTA) model on six evaluation metrics. This demonstrates that our model is capable of generating more detailed and accurate diagnostic reports.
Modified Masking-Based Federated Singular Value Decomposition Method for Fast Anomaly Detection in Smart Grid Systems

The digitalization of production in smart grids entails challenges related to data collection, coordination, privacy protection, and anomaly detection. Machine learning techniques offer effective tools for processing Big Data, but identifying critical system states amidst vast amounts of data remains a challenge. To expedite data analysis, preprocessing through machine learning algorithms becomes essential. This paper introduces the advanced FedSVD algorithm, utilizing Singular Value Decomposition (SVD), which efficiently decomposes large datasets, establishes relationships, and identifies irrelevant data. The algorithm operates in federated machine learning systems, enabling local data processing on private devices while sharing only results with the global learning model. This approach enhances information processing confidentiality and facilitates the exchange of anomaly detection outcomes among network devices. The results of the study demonstrate that the modified FedSVD processing is 5 ms faster on average in comparison to the non-modified one. The proposed FedSVD algorithm calculates anomaly detection with higher accuracy by an average of 1–3% compared to the non-modified FedSVD and SVD ones. The advanced FedSVD algorithm proves to be a decentralized, confidential, and efficient solution for anomaly detection in smart grid systems.
BAMGAN: a KBQA method based GAN and bi-directional attention

Knowledge-based question answering(KBQA) involves using knowledge base technology to generate answers to natural language processing(NLP) questions. KBQA is one of the most challenging tasks in the field of NLP. Answer selection plays a vital role in KBQA, as it requires selecting the correct answer from a pool of candidate answers. However, knowledge bases still struggle to identify the correct answer from multiple candidate answers. To address this issue, In this paper, we propose BAMGAN, a KBQA method based on generative adversarial networks. This method uses generative adversarial networks (GANs) to improve answer selection accuracy in deep neural networks. Experimental results show the effectiveness of the BAMGAN.
Design of multi-label classification model with enhanced feature extraction

This article proposes a sequence-to-sequence based multi-label text classification model. The model combines neural convolutional networks and self-attention mechanisms as encoders, and designs a novel decoder to decode and generate label sequences. The proposed method not only fully considers the interpretable fine-grained information in the source text, but also effectively utilizes this information to generate label sequences. When predicting labels, the global information and local information can be effectively combined to improve the accuracy of label prediction. To verify the effectiveness of the proposed model, this article conducted a large number of comparative experiments. The results show that compared with other models, the model proposed in this article has better performance in different indicators.
Factor Implicit Model of Machine Classification Learning Based on Factor Space Theory

The algorithm proposed in this paper can not only solve the binary classification problem, but can also solve the multi-classification problem. The combination of categories is defined on the basis of the sweeping serial classification algorithm, and the merging serial classification method of factors, both explicit and implicit, is proposed. The algorithm steps are given, and the numerical example is used for analysis. The results show that the proposed combined serial scanning classification method achieves factor concealment and is feasible and practical. The conclusion of factor implicit research on multi-classification learning expands the theory and application of factor space.
Research on code formatting and design and implementation of multilingual code formatting software

In order to solve the problems of disordered code format and inconsistent style in software development, this paper designs and implements software that can format multiple programming languages based on the overview of code format methods. This software can format C/C++/C #/Java/Python, which is a popular programming language nowadays. It also provides a very friendly user interface, which is easy to use and suitable for team development. After testing, the software can accurately format the code and clearly display the format results. It achieves the goal of unifying software style and improving software quality.
A model for face mask detection through deep learning

Since the outbreak of the new coronavirus in China in 2019, wearing masks has gained widespread attention to prevent virus transmission. However, traditional face detection models have struggled to accurately detect faces covered by masks, posing a challenge for public health and security applications. In this study, we propose a novel lightweight model for face-mask detection, called YOLO-ARGhost, which is based on YOLOv4 and incorporates an attention mechanism to enhance accuracy. Our model is designed for fast face-mask detection, overcoming the limitations of previous models. Experimental evaluations on the AIZOO dataset demonstrate that our approach achieves an impressive mean average precision (mAP) of 92.8%.
Decentralized federated learning privacy protection aggregation

The basic idea of federated learning is to aggregate the local model parameters of all participants to obtain a global model, which is more universal and has better performance. However, existing federated learning has the problem of a unique model, where all participants train their local models based on the same global model. However, in different real-world scenarios, data often has different features and distributions, and there is no universal model suitable for learning data from multiple scenarios. Therefore, existing federated learning technologies cannot achieve optimal performance in multiple scenarios. To solve this problem, this paper proposes a decentralized federated learning model where each participant independently selects other models similar to their local models and aggregates them to obtain a specialized model applicable to the local scenario. The model aggregates the parameters based on their similarity with the local model. Models with higher similarity will have a greater weight in the aggregation process. The parameter aggregation is not performed at the central server but at each participant's local device. Through this mechanism, the proposed model achieves specialization of local models for participants and improves performance in different scenarios.