Reviewerly/exHarmony huggingface dataset:
Image Content Reorganization Based on Differential Vision

A variety of colorful patterns can be found in life. They play an important role in housing decoration, landscape design, clothes design, etc. These simple elements make up the pattern. They are grouped together in a number of ways. In the paper, the pattern content organization method based on differential function pattern processing is discussed. The technique can identify the essential elements in the pattern. And the method can get the occlusion order of elements in the pattern. Then create another style of pattern. The method can deal with these patterns with approximate rules effectively. The ingredients in the picture are layered by the differential function. And then, the image content is organized. The method has the advantages of high precision and low computational complexity. The approach studied in this paper improves the distortion and data loss during elements organization. Compared with other image element organization methods, the proposed method is more robust.
Robust Local-Global Feature Representation for Image Emotion Distribution Learning

With the development of online platforms and the increasing social needs of the public, the amount of multimedia data is growing rapidly, especially images. Therefore, it is significant to recognize the emotions represented by them. An image may contain multiple emotions because different individuals may have diverse feelings about the same content. Currently, most works focus on the intrinsic connections among multiple emotion labels to address the unconsidered emotion distribution problem in the traditional single-label methods. However, the image features that truly represent emotions are not taken into account. So in this paper, we extract representative local and global features of images and fuse them, together with the manner of label distribution learning. On one hand, local features are extracted by a spatial and channel-enhanced ConvNeXt network. On the other hand, to avoid the inductive biases of Convolutional Neural Networks (CNNs), we propose a relational reasoning VIT network to extract global features and enhance the connections between different regions of images. The experiments achieve excellent results on the Flickr_LDL and Twitter_LDL datasets, which proves the effectiveness of this method.
Intelligent Attendance System: Combining Fusion Setting with Robust Similarity Measure for Face Recognition

Facial recognition attendance and timekeeping systems are increasingly widely applied in classrooms or businesses. However, when applying the attendance system in real-life scenarios, there are still several challenges, such as the accuracy of predictions, effective data collection methods, and the recognition time on resource-constrained hardware. Most identification methods compare features at the instance level and cannot utilize the aggregated features of all faces with the same attendance. This paper presents an attendance system that utilizes face recognition technology. To enhance the accuracy of face recognition, we employ intelligent data collection techniques and propose improvements to face detection. A clustering algorithm is employed to select relevant facial data for subsequent feature comparison in the intelligent data collection process. By optimizing face detection accuracy, we combine results across multiple frames and introduce a novel approach to information retrieval by combining distances in centroid-based and instance-based settings. Logistic Regression is employed to determine the weights for the method above. We evaluate our combined approach on the VN-celeb dataset, demonstrating its superior accuracy compared to previous centroid-based and instance-based. Furthermore, we extend the application of our proposed attendance system to an intelligent attendance system, utilizing hardware components such as Raspberry Pi 4 and supplementary devices. This system is supported by a database infrastructure and a user-friendly website interface, allowing users to conveniently display attendance-related information.
DSP Implementation of Contrast Enhancement by Nonlinear Diffusion

A real-time image enhancement algorithm is usually needed by an image processing system to improve the visual quality of the captured image sequence. Contrast enhancement by nonlinear diffusion (CEND) in the literature shows excellent performance in artifact removal and local contrast enhancement. However, CEND algorithm is too complex to be implemented on real-time systems. In this paper, in order to achieve real-time image enhancement algorithm based on CEND, we choose C674x DSP (Digital Signal Processing) as targeted device and perform code simplification as well as in-depth code optimization, including compiler-based optimization, calculation simplification and on-chip memory utilization. These optimization measures do not involve access to peripherals, which makes the algorithm stable and convenient to be transplanted to other DSP platforms. We test the program of the CEND algorithm on TMS320DM8168 evaluation board and have verified that the implemented algorithm as a single thread on DSP subsystem is able to achieve above 25fps for 640×480 sized image sequence.
Object Tracking Algorithm Using Siamese Attention Mechanism based Key Point Network

The tracker based on Siamese network usually adopts the cross-correlation of convolutional features between the object branch and the search branch to describe the visual object tracking task as a similarity matching. However, most trackers apply backbone networks to represent multi-scale features in a hierarchical manner, which has limitations in distinguishing similar object. In addition, the feature information on each channel and spatial region is treated equally in the calculation process. In fact, the feature importance of these locations is not the same, and the anchor box detection mechanism requires setting a large number of anchor boxes with redundant hyperparameters, which can lead to computational and memory overload. As for these problems, this paper proposes an object tracking algorithm using siamese attention mechanism based key point network, called SAMR (Siamese Attention Mechanism Reppoints). First of all, the backbone network res2net is used to represent fine-grained multi-scale features on a hierarchical basis for increasing the receptive field of each layer. Then the channel and spatial attention information of the attention mechanism is to enhance the discriminant ability and positioning ability of the feature map. Finally, a set of key points anchor-free frames is adopted to detect and track the object. Experiments on challenging datasets such as VOT2018, VOT2019, and OTB100 illustrate that the SAMR proposed in this paper is superior to other advanced trackers in tracking accuracy.
Long-Tailed Recognition of Food Crop Disease Images Based on Deep Neural Networks

Food crops are very important to people's production and life, and intelligent recognition of food crop disease images is of great significance. However, in the natural environment, the health and disease samples of food crops often have a long-tailed distribution. Long-tailed data is a difficult point for image classification, and insufficient training of tail data often occurs. In this paper, based on transfer learning, the Bilateral-Branch Network is used as the framework to solve the long-tailed recognition problem. Meanwhile, three re-sampling strategies are designed for the Bilateral-Branch Network: balanced re-sampling, smooth reverse re-sampling and reverse re-sampling. After that, we conduct comparative experiments with different model structures and different re-sampling strategies. Finally, we get the conclusion that the Bilateral-Branch Network can effectively solve the long-tailed recognition problem. Among several re-sampling strategies, reverse re-sampling gives the best results. The best result of our method on the long-tailed dataset of food crop disease images is 94.3%, which is 0.7 percentage points higher than that of the traditional single-branch network model under the same conditions.
Enhanced Pixel Privacy: Leveraging Deep Learning for Advanced Image Steganography

Digital image steganography is the practice of safely, undetectably, and retrievably obfuscating data in images. In our increasingly digital world, data breaches are becoming more common, posing a serious threat to data security, including that of digital images shared or stored online. Various steganographic methods have been proposed to address these concerns, largely involving the concealment of a secret image within a host image. Traditional methods, however, have limitations, including restricted data embedding capacity and lower quality of the reconstructed image. This research study proposes and implements a novel model, the Enhanced Pixel Privacy GAN (EPPGAN), which combines a multi-attention-based Swin Transformer with a Cycle GAN training approach. A detailed comparison of the performance of various CNN architectures in hiding a secret image within a host image is performed. The proposed technique outperforms the other existing techniques in terms of image quality and embedding capability.
Unsupervised Medical Image Registration via Dynamic Adaptive Total p-Variation Regularization

The sliding motion of organ edges and smooth motion within the organ co-occur because of the complexity of organ motion during respiration. Many registration methods assume a globally smooth deformation field, ignoring the problem of discontinuities in the field caused by organ sliding motion. In this paper, we propose a new regularization method called dynamic adaptive total p-variance. It was combined with the unsupervised deep learning registration framework VoxelMorph. The proposed approach dynamically estimates the position and magnitude of organ sliding motion from the deformation field during network training and then maps the magnitude to the p-values of Lp-norm (1<p<2). This approach adaptively assigns different exponential values of Lp-norm to each voxel, preserving the smoothness of the internal deformation field and the discontinuity of the deformation field at the sliding interface. The proposed method was tested on a 4DCT dataset of the lung. It outperformed other methods, including smooth regularization and locally adaptive total p-variation, in terms of sliding motion correction and preservation of discontinuities in the deformation field.
Class-Guide Deformable Alignment Fusion for Real-Time Semantic Segmentation

Feature aggregation has an important impact on semantic segmentation, especially the segmentation of object boundaries. With recent advances of various feature aggregation techniques, great progress has been achieved in semantic segmentation. However, it is still a challenge to effectively incorporate high-level semantic features and low-level spatial features. In this work, we propose the Class-Guide Deformable Alignment Fusion network (CGDAFNet) for real-time semantic segmentation. Instead of simply concatenating or adding cross-level features directly, we propose a class-guide deformable alignment fusion module (CGDAFM) to handle the misalignment problem between cross-level features, so as to achieve effective cross-level feature incorporation. A grouped double normalization residual module (GDNRM) is proposed to improve segmentation performance with a small computation cost. Experiment results on the CamVid and Cityscapes datasets verify the effectiveness of our method. Our CGDAFNet can achieve a mIoU score of 78.8% on Cityscapes dataset by taking input with the resolution of 1024×2048. In particular, the speed of our method is 39 FPS with single RTX 3080.
Density-aware Object Detection in Aerial Images

Detecting densely arranged objects is challenging due to the lack of generic definitions and the feature coupling between nearby objects. This paper proposes mathematical definitions of the instance-level, image-level, and dataset-level object density by information theory, called Density Index (DI). The DI shows a high consistency with human perception, serving as a powerful guide for aerial object detection, including data assessment and detector customization. Under the guidance of the DI, we design a DeDet to enhance the detector's performance in detecting densely arranged objects. DeDet pursues accurate location for densely arranged objects by the Density-aware Label Assignment (DLA) and Density-aware Feature Extraction (DFE), conquering the heuristic that the sample assignment and feature extraction are performed independently for each object. Experiments on the DOTA-v1.0 and DOTA-v2.0 show that DeDet can bring a significant improvement to the baseline detector.
A Cycle Architecture Based on Policy Gradient for Unsupervised Video Summarization

This paper proposes a cycle architecture based on policy gradients for unsupervised video summarization. Specifically, the Modified DSNet and DSN-attention net constitute a cycle architecture and promote each other in the training stage to achieve higher performance compared with the unsupervised methods that formulate video summarization as a sequential decision-making process. In the training stage, the DSN-attention net is trained by the policy gradient in combination with the additional MSE loss between the two outputs of the modified DSNet and DSN-attention net. Then the output of the DSN-attention net is taken for generating the labels to train the modified DSNet. As a result, a cycle architecture is built up for unsupervised video summarization. At the test stage, the final video summary is produced by the average fusion of the outputs of both the Modified DSNet and DSN attention net. Extensive experiments and analysis on two benchmark datasets demonstrate the effectiveness of our method and its superior performance in comparison with the state-of-the-art unsupervised methods.
Software Implementation of Augmented Reality Display System for Radioactive Sources and Surface of Nuclear Waste Container

Currently, there are a large number of old facilities facing retirement in the nuclear industry, and it is very important to carry out source item surveys related to retired nuclear facilities. Based on existing radiation source and surface reconstruction algorithms, this paper designs the augmented reality display system for radioactive sources in a container and surface of the container, and packages it into an executable file. It collects the information required for reconstruction and save it into a file in a specified format, and then uses the reconstruction system to reconstruct the radioactive source and three-dimensional surface according to the operation steps, facilitating the operation of technicians. On the basis of adjusting the transparency to observe the specific position of the radioactive source in the container, the intensity information of the radioactive source is added. The intensity of the radioactive source can be determined by observing the colour, which is convenient for technicians to observe the information of the radioactive source.
Robot Motion Planning Based on Improved RRT Algorithm and RBF Neural Network Sliding

Aiming to address the limitations of the traditional rapidly-exploring random tree (RRT) algorithm in robotic arm path planning, such as high randomness, slow planning speed, non-smooth paths, and excessive corners, an improved RRT algorithm incorporating a target bias strategy and artificial potential field approach is proposed. Firstly, this algorithm employs a probabilistic sampling strategy to facilitate quicker growth of the tree structure towards the target point, thus accelerating the planning speed of the RRT algorithm. Subsequently, a repetitive greedy strategy is introduced to reduce redundant nodes in the path and enhance search efficiency. Furthermore, an artificial potential field combined with a target bias strategy is integrated to guide the RRT algorithm towards the target direction. Finally, a third-order B-spline curve optimization is introduced to ensure smoother paths. Compared to traditional RRT algorithms and RRT* algorithms, the improved RRT algorithm exhibited an increase in planning speed of 46.1% and 27.0%, 41.8% and 20.9% for two-dimensional and three-dimensional environments respectively. Path lengths were reduced by 20.9% and 10.6%, 22.0% and 8.7%, while the number of search nodes notably decreased. Considering the complexity of robotic arm operations and uncertainties in system parameters that hinder the establishment of accurate mathematical modeling, a sliding mode control based on radial basis function (RBF) neural networks is proposed. To substantiate the enduring stability of the designed controller, we harness the Lyapunov stability principle. The algorithm validation for path planning and trajectory tracking of the robotic arm are conducted in the MATLAB simulation environment. Experimental results illustrate significant improvements in terms of iteration count, planning speed, path length, and smoothness of the RRT algorithm. Moreover, the motion trajectory of the robotic arm is basically consistent with the expected trajectory.
Not just for play: Why the metaverse should be leveraged for co-creation

Co-creation acknowledges consumers as collaborators rather than mere recipients of products. While Web-based platforms have facilitated the co-creation process by enabling firms to interact with a large number of consumers directly, continuously, and in real-time, this article proposes that the metaverse, which refers to an interconnected network of virtual worlds that can be experienced synchronously by an unlimited number of users, should also be considered as a platform for co-creation. Although many firms are focusing on leveraging the metaverse to increase consumer engagement or enhance retail experiences, this article focuses on its potential to eliminate knowledge and experience boundaries, which has been identified as a limitation of co-creating with consumers. Specifically, the metaverse enables consumers to develop innovative new product ideas by creating immersive environments that blend physical and virtual worlds to provide near-realistic experiences that were once beyond the scope of consumers’ normal experiences. We propose that the metaverse allows firms to incorporate essential elements of co-creation in immersive environments and provide guidelines on how the metaverse can be used effectively by integrating new experiences (extended reality), new motivations (shared experiences), and new partners (Gen Z).
A new method of image encryption using advanced encryption Standard (AES) for network security

Abstract With the rapid increase in the use of technology, images have become a major source of sharing personal, confidential and official information and there is a dire need to protect this secret data. Image encryption plays major role in the security of images and there are many techniques developed for this purpose. Chaos based image encryption has now become most applicable and beneficial technique for image encryption. The purpose of this paper is to highlight a new method of image encryption with the use of advanced encryption standard (AES) and chaotic maps. This technique is composed of substitution and permutation phases. AES is found to be most secure cipher until now against different kinds of attacks. The round keys are generated by AES using key expansion algorithm. The sensitivity of this technique is that it is dependent on initial values and input image. S-boxes in AES introduce non-linearity, confusion, and an avalanche effect, enhancing security and resistance to cryptographic attacks by substituting bytes in the encryption process. The combination of AES and chaotic maps in encryption schemes provides a two-tiered approach to enhance security. AES offers a strong and well-established encryption method, while chaotic maps introduce randomness and complexity, making it more difficult for attackers to decipher encrypted data. This combination is often used to achieve a higher level of encryption security in various applications, including data transmission and storage. Different kinds of analysis and tests are performed on the technique which includes information entropy, number of pixel change rate (NPCR), and peak signal-to-noise ratio (PSNR), unified average changing intensity (UACI) and histogram correlation of adjacent pixels. The results of these tests show that this technique is secure and resistant towards attacks.
Atypical architectural distortion detection in digital breast tomosynthesis: a multi-view computer-aided detection model with ipsilateral learning

Objective.Breast architectural distortion (AD), a common imaging symptom of breast cancer, is associated with a particularly high rate of missed clinical detection. In clinical practice, atypical ADs that lack an obvious radiating appearance constitute most cases, and detection models based on single-view images often exhibit poor performance in detecting such ADs. Existing multi-view deep learning methods have overlooked the correspondence between anatomical structures across different views.Approach.To develop a computer-aided detection (CADe) model for AD detection that effectively utilizes the craniocaudal (CC) and mediolateral oblique (MLO) views of digital breast tomosynthesis (DBT) images, we proposed an anatomic-structure-based multi-view information fusion approach by leveraging the related anatomical structure information between these ipsilateral views. To obtain a representation that can effectively capture the similarity between ADs in images from ipsilateral views, our approach utilizes a Siamese network architecture to extract and compare information from both views. Additionally, we employed a triplet module that utilizes the anatomical structural relationship between the ipsilateral views as supervision information.Main results.Our method achieved a mean true positive fraction (MTPF) of 0.05-2.0, false positives (FPs) per volume of 64.40%, and a number of FPs at 80% sensitivity (FPs@0.8) of 3.5754; this indicates a 6% improvement in MPTF and 16% reduction in FPs@0.8 compared to the state-of-the-art baseline model.Significance.From our experimental results, it can be observed that the anatomic-structure-based fusion of ipsilateral view information contributes significantly to the improvement of CADe model performance for atypical AD detection based on DBT. The proposed approach has the potential to lead to earlier diagnosis and better patient outcomes.