Reviewerly/exHarmony huggingface dataset:
Fast Image Process and Detection of Distorted Blur-readable 2D Barcode

Blurring of two-dimensional (2D) barcode is very common in mobile Internet systems, which often leads to barcode recognition failure. In order to deal with the problem of barcode blur, blur-readable (BR) 2D barcode which can be decoded without deblurring is newly proposed. However, the research on the image processing of BR code are rare and the field of BR code detection and identification is almost blank so far, which impede the use of BR code in practical scenarios. In the application of BR code, distortion problems such as tilt and perspective of the image may lead to difficulty in identifying the BR code. In addition, in some industrial scenarios, we hope that the BR code can be instantly recognized to meet the real-time requirement. In this paper, an efficient image processing and detection method for BR code images with complex background is proposed. We use the ellipse feature of the finder patterns of distorted BR code to locate and correct the BR code images. The experimental results show that our BR code detection method is effective to detect distorted BR code images.
Exposing Recaptured Images with Constrained Convolutional Neural Network

Image acquisition and display technology have greatly developed over the past few years, which make it easy to recapture high-quality images from various display media, such as liquid crystal display screen or printed paper. This operation can be utilized to hide image tampering traces and eliminate image watermarking, which poses serious threat to image security. In order to solve this problem, we proposed a recaptured image detection approach based on constrained convolutional neural network. Firstly, a deep residual module is designed to extract a set of prediction error maps. This module has three identical branches, in which a $1\times 1$ convolution layer is used to learn the correlations between color channels and a following constrained convolutional layer is used to extract prediction error maps. Then we feed the output of this module into consecutive convolution modules to further learn the hierarchical representation to make final decision. Ablation experiments verify the effectiveness of our proposed deep residual module. Comparison experiments on three public databases demonstrate the superiority of our proposed method on detection accuracy.
On the Storage Overhead of Proof-of-Work Blockchains

Permissionless blockchains such as Bitcoin have long been criticized for their high computational and storage overhead. Unfortunately, while a number of proposals address the energy consumption of existing Proof-of-Work deployments, little attention has been given so far to remedy the storage overhead incurred by those blockchains. In fact, it seems widely acceptable that full nodes supporting the blockchains have to volunteer hundreds of GBs of their storage, to store and verify all transactions exchanged in the system. In this paper, we explore the solution space to effectively reduce the storage footprint of Proof-of-Work based blockchains. To do so, we analyze, by means of thorough empirical measurements, how existing full blockchain nodes utilize data from the shared ledger to validate incoming transactions/blocks. Based on this analysis, we show that it is possible for full nodes to locally reduce their storage footprint to approximately 15 GB, without any modification to the underlying protocol. We also discuss other client-side strategies to further reduce the storage footprint while incurring negligible computational overhead on the nodes.
Semi-Supervised Semantic Segmentation of Class-Imbalanced Images: A Hierarchical Self-Attention Generative Adversarial Network

How to train models with unlabeled data and implement one trained model across several data sets are key problems in computer vision applications that require high-cost annotations. Recently, a generative model [1] proves its advantages in semi-supervised segmentation and out-of-domain generalization. However, this method becomes less effective when meet with class-imbalanced images whose foreground occupies small areas. To solve this problem, we introduce a hierarchical generative model with a self-attention mechanism to help with capturing features of foreground objects. Concretely, we apply a two-stage hierarchical generative model to perform image synthesis with the self-attention mechanism. Since attention maps are also semantic labels in segmentation fields, the hierarchical self-attention model can synthesize images and corresponding segmentation labels simultaneously. At test time, the segmentation is achieved by mapping input images into latent presentations with two encoders and synthesizing labels with the generative model. We evaluate our hierarchical model on three biomedical segmentation data sets. The experimental results demonstrate that our method outperforms other baselines on semi-supervised segmentation of class-imbalanced images, and meanwhile, pre-serves out-of-domain generalization ability.
MITPose: Multi-Granularity Feature Interaction for Human Pose Estimation

Human pose estimation is broadly used in action recognition, Re-Identity, and multi-object tracking. Recently deep convolutional neural networks have demonstrated their great power in human pose estimation. However, CNN-based methods are limited by the constrained receptive field that has poor performance in modeling global relationships of different body parts. In this paper, we propose a novel multi-granularity feature interaction network for human pose estimation (MITPose), which exploits the multi-granularity feature interaction in global-local level features, multi-scale features, and locality features. Our MITPose can efficiently leverage the long-range representation ability of transformer net and inductive locality of convolution net to obtain the comprehensive information for key point localization and relationship modeling. Extensive experiments illustrate that our proposed MITPose achieves state-of-the-art performance on the public COCO dataset.
Prior Matching Operator in Self-Supervised Learning

In self-supervised representation learning (SSL), contrastive learning has achieved remarkable successes in recent years. In contrastive learning, the augmented views of the same image are brought closer (i.e., positive pairs), while views from different images (i.e., negative pairs) are separated apart. To perform well, contrastive methods of ten rely on a large number of negative pairs, which is computationally demanding. Intuitively, the negative samples play a role to scatter the representation of samples in space, which guarantees the uniformity of the representation space, thus making it the key to avoiding representation collapse (i.e., constant features). In this work, we propose an interpretable method, in which prior distribution matching is applied to prevent representation collapse, while no negative pair is required. Empirical experiments show promising results of our method yielded by the priors of Gaussian, uniform, and spherical distribution on the CIFAR-10 and CIFAR-100 datasets.
A Distributed Clock Synchronization Protocol for Proof of Stake Blockchains

In a Proof of Stake (PoS) blockchain, all nodes need to be able to recognize approximately synchronized clocks. A PoS blockchain relies on external clocks, and each node synchronizes its local clock according to an external clock using Network Time Protocol (NTP) or a similar protocol. However, this external dependence is undesirable because it reduces the autonomy, sustainability and trustless nature of the blockchain. This paper presents a clock synchronization protocol for slot-based PoS blockchains. The proposed protocol estimates the block propagation time to ensure that the synchronization is closer to that of a real-world clock. In addition, the proposed method considers the clock drift and clock adjustment frequency to achieve more accurate synchronization. Simulation experiments show that the proposed method can synchronize with a time that is closer to that of a real-world clock and with higher accuracy than existing methods.
Interlaced Perception for Person Re-identification Based on Swin Transformer

Extracting discriminative feature presentations is crucial for Person Re-identification (Person Re-ID) tasks. Although convolutional neural networks have powerful feature extraction capabilities, modeling non-local relationships is still challenging for them. A promising strategy is to adopt visual transformer, which excels at modeling long dependencies based on the self-attention mechanism. However, classic visual transformer has shortcomings in focusing on local and fine-grained features. In this paper, we construct a novel and strong Person Re-ID model based on Swin Transformer, which integrates the advantages of both CNN and Transformer. To further enhance the ability to capture long-range dependencies, we introduce Adaptive Split Self-Attention (ASA) module. ASA module improves globally modeling over relations between regions by performing self-attention on the adaptive partitions of the input. Extensive experimental results on several datasets including Market-1501, DukeMTMC, and MSMT17 demonstrate that our method achieves state-of-the-art performance.
Skeleton-Based Dumbbell Fitness Action Recognition Using Two-Stream LSTM Network

With the development of 3D skeleton extraction technology, skeleton-based action recognition has made significant progress in recent years. However, there are few studies on dumbbell fitness action recognition. Therefore, this paper collects a 3D skeleton sequence dataset based on dumbbell fitness (DUM-Action3D) and proposes an anomaly detection method based on clustering local outlier factor algorithm in the process of data sampling. In particular, in terms of feature extraction, this paper proposes a method to extract mixed multi-dimensional features for action classification and designs a hierarchical two-stream fusion LSTM network. Experiments demonstrate that our method is better than the traditional LSTM network and has a more robust capability of learning representations. Furthermore, our method achieves good recognition accuracy and execution speed on the dataset.
Modality-Independent Regression and Training for Improving Multispectral Pedestrian Detection

Multispectral pedestrian detection enables an all-weather detector by fusing the complementary information of RGB and thermal images. However, due to the modality difference and the spatial misalignment between RGB and thermal sensors, the positions of the same pedestrian in two different sensors may be different and some pedestrians may only be visible in one sensor. The existing methods mostly adopt unified annotations to supervise different modalities, this is not conducive to the learning of different modal features. Therefore, we propose a multispectral pedestrian detector based on MBNet with modality-independent regression modules, named MBPNet. It adopts the corresponding modality-independent annotation to train the regression branch for different modalities, separately. Then, a decision-level fusion strategy is introduced to fuse the regression results from different branches. Experiments show that the proposed method outperforms the state-of-the-art methods on the KAIST pedestrian detection dataset.
Cascaded Feature Interaction Network for Stereo Matching

Stereo matching aims at estimating the disparity of the input stereo images, which is further used to recover the geometric information of the scenes. Based on the disparity range hypothesis, most existing methods propose to construct cost volumes with the disparity range and introduce 3D convolutional nerual networks (CNN) to predict the best disparity for each pixel from the given disparity range. Although these methods achieves notable performance on standard datasets, the given disparity range limits their generalization to different scenes with different disparity range. Moreover, the introduced 3D CNN significantly increases the computational complexity for stereo matching systems. In this paper, instead of constructing cost volumes with the given disparity range, we tackle stereo matching task as the feature matching between stereo images with epipolar constraint for general purpose and we thus present Cascaded Feature Interaction Network (CFINet) for efficient stereo matching. Precisely, we first propose Cross Fusion Module (CFM) to fuse cross-image features and model the cross-image similarity for each pixel within epipolar lines. Secondly, we design the Upsampling Fusion Module (UFM) which ensures a cascaded multi-level feature aggregation from low resolution to high resolution, resulting in our coarse-to-fine network architecture with high efficiency. Experiments demonstrate that the proposed method generalize well in different disparity ranges with encouraging efficiency. Specifically, our CFINet is 7× faster than the well-known STTR with lower memory usage and achieves competitive results on SceneFlow and KITTI 2015 datasets compared to cost-volume based methods.
JDSNet: Joint Detection and Segmentation Network for Real-Time Road Scene Understanding

The paper proposed a novel Joint Detection and Segmentation road scene understanding deep Network, named JDSNet, achieving real-time inference with high-accuracy result. Overall, we accomplish this by four components: 1) Based on encoder-decoder network and global pool module, Shared Feature Module is designed to obtain richer multi-scale feature. 2) Adaption Feature Selection Module is developed to learn task-specific features, it’s considered as feature selector from Shared Feature Module. 3) Task Cross Module is proposed to enhance subtask information exchange. 4) Dynamic Weight Average is introduced to average multi-task loss weight over time by considering the rate of change of loss for subtask, to find good balance between tasks. Finally, we evaluate JDSNet on Cityscapes road datasets and produce competitive results compared with the state-of-the-art methods. Specifically, JDSNet achieves 75.3% mIoU and 56.4% mAP on Cityscapes dataset, with speed of 24.2FPS for 1024×512 high-resolution image.
Implementation of Face Recognition Using Discrete Cosine Transform on Convolutional Neural Networks

Authentication in a system is one way to protect user data, several methods to authenticate are with passwords, fingerprints and face recognition. The use of deep learning in face recognition requires relatively large and large datasets. Convolutional neural network (CNN) is the method used in this experiment, but this method requires a large enough storage space so that it becomes one of the problems in terms of data storage. The application of discrete cosine transform (DCT) is one way in terms of image compression to reduce storage space on the platform. The use of the DCT image compression method in this study was carried out to compare the image datasets transported by DCT and those that were not transported to the speed of the learning process and the results of the learning process. From the results of experiments carried out that changes to the activation function and epoch it was found that the application of the sigmoid activation function was the most optimal compared to other activation functions. The application of DCT to all activation functions found that on average the learning process could be completed faster. the accuracy value on the sigmoid both from the dataset with DCT application or not, the accuracy value is above 95%, with the optimal value at the 60th epoch the accuracy value reaches 99.8% without -DCT and 99.6% with DCT.
Similarity Measurement Human Actions with GNN

Measuring the similarity of human actions represented with human skeletons from motion capture plays an important role in classification, retrieval and analysis of actions. In this paper, a similarity measurement method of human action based on graph neural network is proposed. In this method, due to the introduction of graph convolution neural network, the dependence between adjacent joints in human skeleton can be obtained, which makes the expression of human action in a frame more accurate. In the further action similarity measurement, LSTM with self- attention is used to extract temporal feature of the human action sequence, and finally MMD-NCA is used to measure the similarity of action sequences. Experiments on public dataset verify the effectiveness of this method in action recognition and action similarity measurement.
Image Retrieval for Visual Localization via Scene Text Detection and Logo Filtering

Visual localization is a core component of technologies enabling autonomous driving, augmented reality, etc. Image retrieval is a crucial step commonly used in state-of-the-art visual localization approaches. This paper presents a text-based image retrieval and filtering algorithm specifically designed for shopping mall scenarios. Compared to repetitive architectural styles, e.g. walls, floor, columns, which are common in shopping malls, logos of the shops are more informative and distinctive. The characteristics of text regions are fully utilized: (1) features extracted by a CNN network are used to filter out texts with less information, (2) image patches centered on text areas are used for global feature extraction, (3) the size and contact of text areas are used for the ranking of retrieved images when multiple texts are detected in one query image. Our approach can be incorporated into most existing retrieval network generating a global descriptor for the input image. Intensive experiments are carried out on ground-truth image databases, verifying the effectiveness of our approach.
Traffic Police Gesture Recognition Based on Openpose and GRU

In the field of human posture recognition, especially in the field of Automatic driving, the posture recognition of specific occupational groups has always been a research direction of great concern. In this paper, a traffic police gesture recognition system based on Openpose skeleton recognition and Gated recurrent unit GRU is proposed. Openpose real-time multi person two-dimensional pose estimation is used to extract the traffic police gesture skeleton and keypoints, create multiple 15 frame video datasets, record 8 main traffic gestures, and extract the positions of 14 main joint points that have a great impact on traffic police gesture detection. The motion of human body is detected by recording the position change of joint points. With the help of Openpose, the system can effectively recognize human joints in complex environment and improve the accuracy of human posture extraction. The Gated recurrent unit (GRU) is introduced to extract the timing characteristics of traffic police gestures by using the obtained keypoints instead of images. The experimental results show that the system can better recognize the traffic police gestures, and the accuracy of the moving traffic police gestures can reach 91.51% under the complex background.
Bi-Directional Image-Text Retrieval With Position Attention and Similarity Filtering

In recent years, cross-modal retrieval has gradually become the frontier and hotspot of academic research at home and abroad, and it is an important direction for the future development of information retrieval. Some current methods improve the performance of image-text retrieval by exploring more comprehensive global image-text alignment information or capturing region-word local fine-grained alignment. However, previous methods did not mine more useful information to obtain more accurate matching scores. In this paper, we propose a location attention and similarity filtering network for image text retrieval. Specifically, we enhance visual-text joint embedding learning with global and local alignments. We then enhance more reliable relationships between images and text sentences by exploring the location information of objects in images through location attention. In addition, we use a similarity filtering mechanism to selectively focus on important and representative alignment information while leaving the distraction of meaningless alignment information aside to effectively integrate these alignments. Experiments on our proposed method on the public datasets Flickr30K and MS-COCO validate the effectiveness and superiority of our method.
Research on Pleasure Experience Design of Housekeeping APP for Middle-Aged and Elderly

To cater for the development of "Internet + housekeeping" and the current situation of China's aging society, to extract the influencing factors of middle-aged and elderly users on the pleasure experience of housekeeping APP through social investigation, and to provide effective design suggestions for the pleasure experience of middle-aged and elderly housekeeping APP. Using semi-structured interview for middle-aged and old users of domestic APP cheerful experience factor, further comb older users in the use of domestic APP cheerful experience design elements, on the basis of the questionnaire using factor analysis method, divided into domestic APP for middle-aged and old user cheerful experience design dimensions and its correlation, In this way, it can objectively guide the pleasure experience design of housekeeping APP. By exploratory factor for Service expectations, Elderly cognitive, Intend to use, Social interaction four dimensions of social interaction, It is further concluded that the improvement of social interaction and cognitive fitness for the elderly have a direct impact on the pleasure experience of housekeeping APP and the survey results are analyzed to guide the practice cases of pleasure experience design of housekeeping APP, providing certain reference value for the pleasure experience design of middle-aged and elderly housekeeping APP.
Authenticated Multi-Version Index for Blockchain-based Range Queries on Historical Data

With the rapid growth in emerging applications driven by blockchains, there is an increasing need to support efficient ad hoc querying of authenticated historical data to enable rigorous security and regulatory requirements. The existing blockchain systems offer little or no support for query capabilities over historical data. Although, a full blockchain archive node can be used to maintain historical data of all executed transactions' off the chain, this is not scalable due to data volume. Moreover, such ‘offline’ historical data lack tamper evidence support. To address the above issues, we introduce an authenticated index structure called Authenticated Multi-Version Skip List (AMVSL), designed to support a rich set of query features over historical blockchain data. We further present three range queries: SVRK, MVRK and MVAK, which offer querying over a range of keys and a range of versions. Our experimental evaluation demonstrates that AMVSL can efficiently support these queries and can achieve better performance over existing authenticated data structures.
A Denoising Method of Human Pose Estimation Based on Reconstruction from Keyframes

In the field of 3D human motion reconstruction, it is always the case when jitters on 2D key points corrupt 3D human pose visualization. Hence, denoising is necessary dealing with 3D key points generated by neural networks. Contrary to traditional methods, a post-processing denoising method based on reconstruction from keyframes is proposed, giving consideration to both smoothness and reconstruction accuracy. This approach converts key points to bones’ rotation angles to avoid bone length skewing in post-processing. Afterwards, it selects keyframes with acceleration constraint, which is insensitive to jitters, ensuring the accuracy of keyframe extraction. Finally, Squad spline interpolation is implemented within keyframes in order to smooth human motion curves. Extensive experiment results demonstrate that this method can, guaranteeing accuracy of human poses, remove jitters within motions, increasing smoothness of motion curves, and eliminate bone length skewing in produced by filters completely. For complicated motion data, such as dances, and different noise patterns, this method works stably as well.