Reviewerly/exHarmony huggingface dataset:
Research on Weighted Association Rules Mining Algorithm for the Medical Internet of Things Information Based on Fuzzy Numerical Constraints

With the wide application of network communication technology and automatic data acquisition tools, the automation level of the medical service industry is getting higher. A weighted association rule mining algorithm supporting fuzzy numerical constraints (FQC wed Apriori algorithm) is proposed. Firstly, the association rules between transactions in the database are found. Moreover, the fuzzy association rules are mined by the fuzzy query. What' s more, the frequent itemsets are found combined with the improved weighted association rule algorithm, which can discover the associated information in the process of medical service and evaluate the effectiveness of the mining algorithm effectively.
Image Segmentation for Autonomous Driving Using U-Net Inception

Assistive Driving System is part of all modern vehicles; it can reduce the number of traffic accidents and avoid potential loss of human life. For optimal performance, ADS should be aware of the objects present in its surroundings and detect the location of each of them by using image segmentation. Image segmentation is the classification of images into semantic classes on a per-pixel level. It is also widely used in autonomous driving, in which models are trained on the mask image obtained from semantic segmentation. The paper aims to utilize state-of-the-art image segmentation models (U-Net and GoogLeNet) for training on the Mapillary Vistas Dataset. While there are trained models on other datasets, Mapillary Vistas is explicitly designed for image segmentation in the application of autonomous driving; hence, the image from the dataset provides a real-world representation of street view compared to other popular datasets. Furthermore, Vistas has images from all parts of the world. Therefore, the model can learn robust features that allow it to perform image segmentation regardless of the geographical location and weather conditions.
DEVTrV2: Enhanced Data-Efficient Video Transformer For Violence Detection

In smart surveillance systems, event classification and detection are essential parts, on the other hand, violent event recognition is one of the most important key elements in that systems. The use of the transformer network for video action recognition has achieved very high results, On the other hand, the transformer network needs a large amount of data to gain good accuracy therefore, a new family of video transformer networks called Data-efficient video transformer (DEVTr) came to address these problems and shows that the use of the pre-trained convolution neural network in the embedding stage can improve accuracy with the use of a small dataset for the given task. In this study, we extend the data-efficient video transformer (DEVTr) for better event detection within a small dataset and with low hardware resources. We also introduced two new video data augmentations methods (random erase from frames and frame position shifting with blurring). The model achieved 98.25% accuracy on the Real-life violence dataset (RLVS), an accuracy of 96% on the NTU CCTV-fight dataset, and accuracy of 91.803% on the UBI-Fight dataset. A comparison with previous techniques illustrated that the proposed methods provide the best result among the other research for violence event detection within a small dataset and low hardware resources.
Review of <i>Formal Methods: An Appetizer</i>

There are numerous methods of formally defining the semantics of computer languages. Each method has been designed to fulfil a different purpose. For example, some have been designed to make reasoning about languages as easy as possible; others ...
Object Detection in Optical Remote Sensing Images Based on Improved Lightweight Neural Network

The optical remote sensing images collected by Unmanned Aerial Vehicle Remote Sensing (UAVRS) with real-time information, and object detection of the optical remote sensing images has significant development potential in the many fields such as transportation and agriculture. In addition to large objects such as buildings, small objects such as vehicles and ships can also be clearly observed in the collected high-resolution remote sensing images. This paper mainly focuses on the detection of vehicles and ships in remote sensing images, and proposes Scene-SSD based on the main principles of MobileNetV3 and SSD. In this paper, we improve the basic block bottleneck of MobileNetV3, introduce Generalized Focal Loss (GFL) function to replace the original loss function in SSD, improve the class imbalance problem and make the bounding box estimations are more precise, and the network model is trained by transfer learning to improve its generalization ability. It is experimentally illustrated that in object detection of remote sensing images, the Scene-SSD proposed in this paper is fast and the tested mAP can reach 77.9%, which is better than the MobileNetV3-SSDLite with the same network structure in the comparison test.
DenseATT-Net: Densely-Connected Neural Network with Intensive Attention Modules for 3D ABUS Mass Segmentation

Accurate segmentation of breast mass in 3D automated breast ultrasound (ABUS) images is important in breast cancer analysis. However, it is hard to obtain enough labeled ABUS images for training segmentation networks, which may lead to over-fitting problem in deep learning based methods. Aiming at this problem, a lightweight segmentation network D2U-Net is selected as the baseline. ABUS images have a low signal-to-noise ratio and serious artifacts, which makes mass boundary unclear. To address this problem, different kinds of attention modules are inserted into the segmentation network. These attention modules include spatial attention, channel attention, convolutional block attention module (CBAM) and squeeze-and-excitation (SE) block. The whole segmentation network is termed as DenseATT-Net. An ABUS dataset with 170 volumes is employed to verify the segmentation performance. Experimental results show that the proposed method performs better than other segmentation models on 3D ABUS images.
IBR-Yolov5: Illegal Building Recognition with UAV Image Based on Improved YOLOv5

The urban illegal buildings have seriously disrupted the urban land planning and development space, even having serious security risks. How accurately and comprehensively identifying the illegal buildings is very important for their management and planning. The traditional method of identifying illegal buildings is inefficient and high consumption. Learning-based methods can improve efficiency and save resources but a high miss rate. In response to the above problem, this paper proposes an illegal building recognition method based on UAV (Unmanned Aerial Vehicle) images: IBR-Yolov5 (Illegal Building Recognition with UAV Image Based on Improved YOLOv5). IBR-Yolov5 appends SPP (Spatial Pyramid Pooling) based on stochastic pooling to the Backbone module. Meanwhile, CBAM(Convolutional Block Attention Module) is used to highlight the main features and suppress irrelevant features, which can ultimately improve the detection accuracy of IBR-Yolov5. In addition, IBR-Yolov5 combined with BiFPN (Bidirectional Feature Pyramid Network) can fuse features extracted by different layers of networks to reduce feature loss. Experimental results show that the miss detection rate of the proposed improved model is lower in comparison with the original YOLOv5, and detection precision has been improved.
Generative Adversarial Networks Based on Dynamic Word-Level Update for Text-to-Image Synthesis

The traditional fine-grained generation adversarial networks pay less attention to the importance evaluation of word-level information for text-to-image synthesis. Incorrect importance rating of word-level information may skew image generation to less critical direction and affect the generation of key features. In this paper, a novel generative adversarial network based on dynamic word-level update is proposed to solve the above problem by dynamically updating the different importance of each word in the image generation stage. The assignment module with dynamic weights is designed to update text features and image features by communicating the information of text and image. This module enables the importance rating of words to be updated. In addition, the mixed zero-center gradient penalty function and visual loss function are proposed to optimize generative adversarial networks based on dynamic word-level update. The mixed zero-center gradient penalty function allows the generator to generate image with high semantic consistency and ensures the stability of the training process. The visual loss function further improves the visual effect of the generated image by narrowing the difference between the real image and the generated image. Extensive experiments on public benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art methods.
A Matrix Inversion Method Based on LDLT Decomposition and its Application in STAP

This paper presents matrix inversion algorithms based on LU decomposition and QR decomposition and LDLT decomposition (i.e. improved Cholesky decomposition) and the time complexity of the three methods is analysed. It is found that the inverse algorithm based on LDLT decomposition has the lowest complexity. Considering the resource consumption problem in practical engineering, this paper proposes the LDLT decomposition-based matrix inversion algorithm with partial double precision data type and the algorithm performance is simulated. It is finally found that the partial double precision algorithm for calculating the inverse matrix reduces resource consumption, improves operational efficiency and is within an acceptable margin of error from the true value, which is suitable for engineering Implementation.
An Optimization Scheme of Object Detection Model Based on CNN Feature Visualization Method

In the field of computer vision, enhancing the interpretability of networks is a hot research issue. We proposed a feature visualization interface based on Grad-CAM algorithm, which can match the convolutional neural network under Pytorch framework and TensorFlow framework. We added the SE attention module and SPP module and analyzed the phenomenon of object detection by using the feature visualization interface. At the same time, considering the deployment on resource-limited equipment, 8.9M and 109M YOLOv3s and YOLOv3m models are designed, and the phenomenon of object detection is analyzed by using the feature visualization interface. When the model size of YOLOv3s model is reduced by 96.2%, the accuracy of object detection can reach 0.676, which meets the deployment condition of resource-limited equipment.
Improved Binarization Algorithm of DM Code Image under Complex Illumination

The DM (Data Matrix) two-dimensional barcode images collected on metal parts with complex lighting conditions are prone to low contrast and local shadow, and the problem of uneven brightness leads to the loss of two-dimensional barcode information, which brings difficulties to the subsequent processing. However, the existing binarization algorithms will have the problems of too much noise and long segmentation time when dealing with this kind of images. To solve these problems, an improved local threshold algorithm is proposed to improve the anti-noise performance and computational efficiency. First of all, the algorithm model is improved to reduce the sensitivity to noise, and then the integral image algorithm is used to speed up the calculation of the local mean. In addition, the gray value of the local central pixel and the local mean are used to adjust the threshold adaptively according to the gray change characteristics of each pixel neighborhood, so as to further improve the segmentation efficiency. The improved local threshold algorithm and other five algorithms are tested on images with complex illumination conditions. The results show that the improved local threshold algorithm can not only effectively overcome the influence of complex illumination, has a good anti-noise effect, but also greatly shorten the time of image segmentation.
Image Tamper Detection Based on Two-Stream Attention Faster R-CNN

In the past, image tamper detection was mainly based on the traditional way of manually designing features for feature extraction. In many recent studies, image tamper detection based on deep learning has achieved excellent results. We propose an end-to-end image tamper detection model called Two-Stream Attention Net (TSA-Net) based on RGB-N and Faster R-CNN, where the Region of Interest (RoI) pooling in RGB-N is replaced with RoI align. In the training process, by using the constrained convolution layer and steganalysis rich model (SRM) filter to extract the image noise and adding the attention module to the model, our model can significantly enhance the locating of the boundary of RGB image and the noise area where the noise image is inconsistent. In the testing process, in order to reduce false negative, we replaced standard Non-Maximum Suppression (NMS) by using Soft-NMS. Through the use of the attention module and Soft-NMS, the detection ability of the model in small targets is improved. After experiments on four standard datasets, the model significantly surpasses the original model in the detection of image splicing, image copy-move, and image removal.
A Fast Heterogeneous Image Registration Method Based on the Human Skeleton Points

Visible-infrared composite cameras are widely used in public health monitoring and intelligent security. Fast and accurate registration of visible-infrared images is the key to the further application of the information in different spectra. In the crowd scenario, aiming at the problem of poor consistency of traditional feature points such as corners, spots, and contour points in the two types of images, this paper proposes an effective method of heterogeneous image registration based on human skeleton points. Firstly, the human skeleton points are detected by a multi-person pose estimation network, which represents the common features of visible and infrared images decently. Then, the key points are matched by a top-down method that integrates the spatial information of point groups with the type of each point. Finally, the point pairs are put into the control points buffer and the transformational matrix is calculated. We capture and make a visible-infrared image sequence dataset in a crowd-oriented scene, and conduct qualitative and quantitative tests on it. The experiments show that the designed method outperforms traditional registration algorithms both in terms of accuracy and processing speed.
A Robust Approach for Smile Recognition via Deep Convolutional Neural Networks

Smile recognition is a difficult research issue in the fields of computer vision and pattern recognition. Most of existing algorithms are only suitable for western people's smile recognition in simple backgrounds, and cannot well recognize Chinese people's smile in complex backgrounds. In order to solve this problem, we first construct a dataset composed of 4,000 western face images and 4,000 Chinese face images. Especially, 5,000 images in this dataset have complex backgrounds. Then, we use this dataset to train a convolutional neural network, a residual neural network, and a lightweight neural network for smile recognition, respectively. Various experiments show that our algorithm has a good generalization ability to recognize the smile of both western people and Chinese people robustly even in complex backgrounds.
Image Dehazing via Pyramid Exposure Fusion

In this paper, we present a new image dehazing method via pyramid exposure fusion. Instead of relying on the accuracies of global atmospheric light and trans-mission separately as most previous methods, our method directly estimates the effective range of the atmospheric light, and then constructs initial dehazing images with different exposure. Finally, we propose a pyramid exposure fusion via adaptive layer selection to improve the visual dehazing effect. Experimental results show that our method outperforms state-of-the-art haze removal methods in terms of both subjective and objective dehazing visual effect
Integrating Group Signatures in Complex Decentralized Marketplace Transactions for Improved Buyer Privacy

Marketplace applications are a popular application category for blockchains because they allow the possibility for parties to transact without the need of a trusted middleman entity. Supply chain marketplaces involve buyers and sellers of goods and services that interact using complex transactional workflows rather than single, one-off transactions. Further, in some contexts, there is a need to maintain some degree of privacy about which parties are transacting and/or the nature of their transactions. Keeping such information private is often crucial for maintaining competitive advantage. However, traditional public key signature schemes do not provide strong enough privacy guarantees for such applications. On the other hand, signature schemes like Group Signatures offer more robust privacy guarantees but have not yet been adopted broadly in existing platforms. However, supporting privacy across multiple transactions in a workflow requires some extension to existing group signatures as opposed to just for a single transaction. In this paper, we present our effort on implementing some existing theoretical proposals for Group Signatures. We focus on application contexts in which it is sufficient to “hide” the originator of a transaction workflow i.e., the buyer (but is easily generalizable). Because, transaction workflows involve “multiple” related transactions which will be obfsucated by introducing privacy, we extend a group signature scheme with “linkability” of related transactions in a workflow under anonymity. We integrate our extended signature scheme into SmartChainDB - our extension of an open source blockchain platform, BigChainDB, that introduces several features for enabling complex marketplaces on blockchains.
Jetson NX-oriented Armored Vehicle Fine-grained Recognition

Armored vehicles are the main combat equipment on the land battlefield. After discovering armored vehicles, the first task is to identify their fine-grained types, so as to provide more accurate intelligence information for commanders and provide auxiliary decision support for subsequent actions. At present, the research of armored vehicles mainly focuses on target discovery, but ignores the accurate recognition and edge application conditions after discovery. Therefore, to classify the fine-grained armored vehicles, a lightweight adaptive enhanced recognition model is proposed for Jetson NX edge computing device. The model adopts the lightweight RepVGG as the backbone, adds an adaptive enhancement mapping module to the linear classifier head, and transforms the linear classifier head into a nonlinear classifier head, which improves the adaptability of the model in cross-domain transfer learning. At the same time, facing the carrying capacity and power consumption limitation of the combat platform, the re-parameterization technology and the TensorRT optimizer are used to realize the compression, transformation and speed-up of the model on Jetson NX. On the armored vehicle fine-grained recognition dataset, the proposed method achieves good accuracy and fast computational speed.
Dynamic Sign Language Recognition Based on Improved R(2+1)D Algorithm

Previous sign language recognition networks faced problems with model computation and training time high requirements. To address these problems, a sign language recognition method based on 3D convolution and (2+1)D convolution is proposed. First, image enhancement is applied to the dataset based on wavelet transform to speed up the convergence of the model. Then, a dynamic sign language recognition network based on Spatio-temporal convolutional network R(3+2+1)D is designed. The residual module of the R(2+1)D network model is improved using the idea of pre-activation structure, and the original down-sampling residual unit is reconstructed using a pooling operation with step size 2 and 3D convolution with size 1 to reduce the model size and improve the recognition rate. Finally, comparison experiments are conducted on the Chinese Sign Language dataset (CSL). The experimental results show that compared with the original network, the recognition speed of this method can be improved by 68.1% and the recognition accuracy by 1.5%, while the memory occupation is reduced by 52.4%.
Improved Algorithm for Defogging of Sky Images Based on Dark Channel Prior

The dark channel prior (DCP) has a good defogging effect on single image, but when the image contains a large area of sky, the defogging image will have some problems such as over bright color and distortion in the sky. Based on DCP, an improved algorithm for superpixel segmentation and transmittance compensation is proposed in this paper. Firstly, the simple linear iterative clustering (SLIC) in superpixel segmentation is used to divide the image into several superpixel regions with similar characteristics such as color and scene depth, and then, these superpixels are used to replace the fixed rectangular window used in the minimum value filter in DCP, so as to avoid blocking effect and halo phenomenon caused by discontinuous scene depth. Then the transmittance compensation function is constructed to compensate the transmittance of sky area, so as to make up for the inaccurate estimation of the transmittance in DCP. Finally, we evaluated the proposed method using realistic haze images and O-HAZE datasets. The experimental results show that, for the image which contains a large area of sky, the algorithm proposed in this paper not only ensures the dehazing effect, but also overcomes the defect that the sky area doesn’t conform to the dark channel prior rule, and achieves a better visual effect.
A Sealed-bid Auction with Fund Binding: Preventing Maximum Bidding Price Leakage

In an open-bid auction, a bidder can know the budgets of other bidders. Thus, a sealed-bid auction that hides bidding prices is desirable. However, in previous sealed-bid auction protocols, it has been difficult to provide a “fund binding” property, which would guarantee that a bidder has funds more than or equal to the bidding price and that the funds are forcibly withdrawn when the bidder wins. Thus, such protocols are vulnerable to false bidding. As a solution, many protocols employ a simple deposit method in which each bidder sends a deposit to a smart contract, which is greater than or equal to the bidding price, before the bidding phase. However, this deposit reveals the maximum bidding price, and it is preferable to hide this information. In this paper, we propose a sealed-bid auction protocol that provides a fund binding property. Our protocol not only hides the bidding price and a maximum bidding price, but also provides fund binding, simultaneously. For hiding the maximum bidding price, we pay attention to the fact that usual Ethereum transactions and transactions for sending funds to a one-time address have the same transaction structure, and it seems that they are indistinguishable. We discuss how much bidding transactions are hidden. We also employ DECO (Zhang et al,. CCS 2020) that proves the validity of the data to a verifier in which the data are taken from a source without showing the data itself. Finally, we give our implementation which shows transaction fees required and compare it to a sealed-bid auction protocol employing the simple deposit method.