Reviewerly/exHarmony huggingface dataset:
Online Voting System using Blockchain

The crucial right to cast a vote or essentially casting a vote on a ballot forms the basis of democracy. From ballot boxes, to punch cards the voting systems are periodically transformed into EVMs for conducting elections. But, the existing e-voting systems are vulnerable to hacking, have limitations and risk of computer technologies, and needs physical security of machines as well. So, the main focus of the proposed system is to overcome these issues and introduce secure, user-friendly Online Voting. The proposed model enhances the level of security by making use of blockchain in which it is not possible to manipulate votes. Blockchain is used for storing votes and it gives sufficient security which minimize the chances of dummy votes. Cryptography and Face ID are also adopted for verification purposes. The presented online portal is highly secure and also has other unique security features such as primary id generation that puts another layer of security and enables the admin the ability to check the user information and to decide whether he is qualified to vote or not.
Customized YOLOv3 model for face detection in crowded images

Face detection (FD) is an essential stage in many applications like face recognition, object detection, and class attendance systems. The accuracy of the FD system affects the further processing stages in those applications. Therefore, the reliable FD system should give all faces that exist in the input image, and the corresponding bounding boxes as well. The standard methods for the FD system work well for frontal faces. However, those methods have a low accuracy where faces are blocked, blurred, or different in scales. In this work, hyper-parameters of the you-only-look-once version 3 (YOLOv3) algorithm are customized and used for FD in crowded images. Firstly, the dimensions of the anchor boxes is fine-tuned to be more appropriate to detect different face scales in crowded images. This customization gives better performance for YOLOv3 comparing with the predefined anchor boxes in this algorithm. Secondly, the dimensions of features on the last detection layers are reduced to enhance the detection speed. Finally, the proposed model is compared with other vision-based and deep learning-based FD algorithms using real-life crowded images. The conducted experiments on the Google open images v4 dataset show that accuracy of the customized YOLOv3 is 94.2% which outperforms the other models.
Mr_Kru: Accelerator of Kruskal’s Algorithm for Finding Root Node in Parallel

Minimum Spanning Tree(MST) is widely used in VLSI design, network routing, human brain network analysis and other fields. Kruskal’s algorithm is one of the classical methods to find the minimum spanning tree. Aiming at the problem that the root node search time accounts for a high proportion of the Kruskal’s algorithm, according to the characteristics of real world graph data, this paper presents a hardware accelerator Mr-Kru for the Kruskal’s algorithm to find the root node in parallel based on the Coordinate(COO) compression format. In this paper, the prototype system is built on the Virtex UltraScale+VCU118 evaluation platform, and complete the hardware testing of Mr_Kru based on four different datasets. The experimental results show that the running time of parallel mode is 34.9%~41.6% lower than that of serial mode. The resource cost is 97.6% and 99.6% lower than that of the comparative literature. Vivado simulation power consumption is 2. 671W.
Block Importance Mapping for Video Encoding

This paper presents a novel video encoding algorithm called Block Importance Mapping. In this method, blocks are assigned a quantization parameter, QP, offset based on how likely the block samples are to be used as references in encoding of nearby pictures. The reusability estimation is based on a motion search in pictures immediately before and after the current picture in output order. Blocks that are likely to be used as reference blocks are coded with a lower QP value, i.e., higher quality, whereas blocks that are deemed unlikely to be referenced are coded with a higher QP value. This method has been implemented in the VVC reference software VTM and tested on various configurations and contents. It is reported to provide BD-rate savings of 1.93% for PSNR and 3.69% for MS-SSIM in Random Access and 2.26% for PSNR and 4.29% for MS-SSIM in Low Delay coding. For HDR content, wPSNR BD-rate savings of 1.18% in Random Access configuration is reported.
Graph_CC: Accelerator of Connected Component Search in Graph Computing

The connected component algorithm is an important basic algorithm for graph computing, which can be applied to scenarios such as network robustness and social network analysis. The connected component algorithm requires high data access and computation overhead to traverse the entire graph to find all connected components, resulting in intensive random memory access and inefficient use of on-chip memory. In order to solve this problem, this paper studies the influence of three different data organization forms on the cache locality of CC algorithm, and selects the storage form of CSCI format with better cache locality. On this basis, this paper designs a connected component search accelerator that can achieve fast addressing: Graph-CC, which stores indexes and data in separate partitions. Through address calculation, sequential addressing can be achieved, and the addressing mode of data in memory is optimized. Using ego-Facebook, p2p-Gnute11a05, and wiki-Vote graph datasets to complete the prototype test of Graph-CC based on the XCVU9P-L2FLGA2104 development board, the results show that compared with accelerators such as ThunderGP, the computing resources are reduced by 22.48%, and the storage resources are reduced by 35.16%, while the throughput is increased by up to 2.75$\times$, and the power consumption efficiency is up to 25.4$\times$.
Near-lossless Point Cloud Geometry Compression Based on Adaptive Residual Compensation

Point cloud compression (PCC) is a crucial enabler for immersive multimedia applications since point cloud is one of the most primitive forms for representing 3D scenes and objects. Recently, some approaches are proposed to improve the average reconstruction quality of octree-based Geometry-based Point Cloud Compression (G-PCC). However, it is noticed that these approaches suffer considerable loss in terms of point-to-point (D1) Hausdorff distance when compared to G-PCC (octree). Here we introduce a near-lossless point cloud geometry compression method based on adaptive residual compensation by adding and removing points with large errors. It allows controlling of D1 Hausdorff (D1h) distance and maintains a great improvement in average reconstruction performance over G-PCC. Experimental results verify the effectiveness of our method, where our method achieves an average of 78.5% D1 and 11.4% D1h Bjontegaard-delta bitrate savings over the octree-based G-PCC on solid point clouds of the MPEG Cat1A dataset.
Video Quality Assessment based on Quality Aggregation Networks

A reliable video quality assessment (VQA) algorithm is essential for evaluating and optimizing video processing pipelines. In this paper, we propose a quality aggregation network (QAN) for full-reference VQA, which models the characteristics of human visual perception of video quality in both spatial and temporal domain. The proposed QAN is composed of two mod-ules, the spatial quality aggregation (SQA) network and the tem-poral quality aggregation (TQA) network. Specifically, the SQA network models the quality of video frames using 3D CNN, taking both spatial and temporal masking effects into consideration for the modeling of the perception of human visual system (HVS). In the TQA network, considering the memory effect of HVS facing the temporal variation of frame-level quality, an LSTM-based temporal quality pooling network is proposed to capture the nonlinearities and temporal dependencies involved in the process of quality evaluation. According to the experimental results on two well-established VQA databases, the proposed model could outperform the state-of-the-art metrics. The code of the proposed method is available at: https://github.com/lorenzowu/QAN.
Tire Pattern Image Classification using Variational Auto-Encoder with Contrastive Learning

Tire pattern image classification is an important computer vision problem in pubic security, which can guide policeman to detect criminal cases. It remains challenge due to the small diversity within different classes. Generally, a tire pattern image classification system may require two characteristics: high accuracy and low computation. In this paper, we first assume that capturing rich feature representation will benefits tire classification and learning through a lightweight network will improve computing efficiency. We then propose a simple yet efficient two-stage training mechanism: 1) We learn a feature extractor using a V ariational Auto-Encoder framework constrained by contrastive learning, projecting images to latent space owing rich feature representation. 2) We train a single-layer linear classification network depend on the features extracted by the previous trained encoder. The Top-1 and Top-5 accuracy on tire pattern dataset is 89.8% and 96.6% respectively, validating the effectiveness of our strategy.
High-frequency guided CNN for video compression artifacts reduction

In this paper, we propose a high-frequency guided CNN for video compression artifacts reduction. In the proposed method, high frequency component in Y channel is extracted and used to guide the quality enhancement of all Y, U, V channels. As high frequency component contains the edge and contour information of the objects in the image, which is of vital importance to both subjective and objective quality. In general, the proposed method consists of two modules: the high frequency guidance module and the quality enhancement module. The high-frequency guidance module uses multiple octave convolutions to extract the high-frequency component in Y channel and then fuse it into the features of Y, U, and V channels. While in the quality enhancement module, multiple CNN residual blocks are used for the quality enhancement of Y, U, and V channels. The proposed method was integrated into both HM-16.22 and VTM-16.0. The results on the JVET test sequence under All Intra configuration shows the effectiveness of the proposed method. Compared with HEVC, the proposed method achieves the average BD-rate reductions of -12.3%, -22.7% and -23.5% for Y, U and V channels respectively. Compared with VVC, the average BD-rate reductions are -6.7%, -12.3% and -13.2% correspondingly.
A efficient predictive wavelet transform for LiDAR point cloud attribute compression

In this paper, a new predictive wavelet transform (PWT) is proposed to solve LiDAR point clouds attribute compression. Our method is a combination of predictive coding and Haar wavelet transform. Based on the spatial information, a hierarchical predictive transform tree is designed to represent 3D irregular data points efficiently. Each level node is classified as a predictive node (P-node) or a transform node (T-node) according to the distances to its adjacent nodes. Then in a top-down coding process, the Haar transform is applied to all T-node pairs, and predictive coding is processed on all P-nodes alternately. It is shown by experimental results that the proposed PWT method offers better R-D performances compared with state-of-the-art methods.
Weak Supervision as an efficient approach for Automated Fault Detection

The real-life applications of machine learning and artificial intelligence has expanded in huge amounts in the past few years. Owing to the rapid developments and advances in various machine learning and deep learning models, developers are now able to achieve futuristic accuracy on standard datasets without any need of manually devised features. However, there is a big obstacle: these models are hungry for data and require large amount of labeled data which ultimately depend on immense human efforts for labeling. The complexity of deep learning models is very high as compared to many of the conventional deep learning models and contain large number of parameters and computations. As a result, these models require large training data to suffice efficient learning. The task of labeling such huge training sets manually is very costly and equally time-consuming. Creating any standard/benchmark dataset might take months or years and may require domain expertise. Also, such dataset which is hand-labeled is difficult to modify and restructure for new purposes. Due to all the above-stated reasons, various types of weak/noisy supervision techniques are being explored and used by developers and scientists, such as using heuristics for labeling training data with the help of external knowledge bases and meta information, patterns/rules, or other CV based classifiers. This paper begins by discussing techniques in machine learning that address the problem of manual-labeling of training data, and then describe our research on modeling, developing, and integrating a varied set of weak supervision sources for labeling unlabeled data obtained from production plants for the automated optical inspection task. In this research, a rivets dataset is used which consists of 4,56,100 images of defective and non-defective magnetic rivets of a control panel with very less labeled data (39,466 images). The dataset consists of images of three batches collected over different time intervals and hence, images across batches have different contrast and luminous intensities. Also, the batches have different amount of class imbalance. We have developed a weak supervised learning pipeline containing weak-supervision sources to obtain weak labels for the unlabeled data and used various consensus strategies to obtain final weaklabels. Later, we trained a CNN model with the weak labels as final labels and compared the results with the model trained with only labeled data.
Design and Optimize of Campus Nucleic Acid Tests Queuing System

Covid-19 is more likely to spread in campus than it in other places because students live together without masks. In this case, it is necessary to take nucleic acid tests in a unified time regularly. To make nucleic acid tests efficient and convenient to manage students and the testing time, this article would apply queuing theory to design a nucleic acid tests queuing system by using the data from Sanda University in April 2022. According to the special conditions on campus, such as course schedule, students' daily activities, and campus management, students would be grouped by several management styles. The system would calculate the start time and waiting time for each group and would strive to take nucleic acid tests in an orderly manner with minimal waiting time.
Assessment of Learning Outcomes in Distance Learning Using the Moodle Platform

The testing on the Moodle platform as a means of assessing the achievement of program learning outcomes in distance is researched on the example of mathematical disciplines at the Kremenchuk Mykhailo Ostrohradskyi National University. In the context of a pandemic, there is an intensification of distance learning. In connection with this, various platforms are being created for organizing online training. The krnu.org system on the Moodle platform is a modern distance education service that allows, first of all, to organize effective control of knowledge using a wide range of different tasks. The assessment criteria are clear and understandable for students. Testing makes it possible to check the programmatic learning outcomes, which is shown by the example of the study of mathematics by students of engineering specialties. The types and functions of testing in engineer's mathematical training in distance learning on the Moodle platform are investigated. A diagram of testing in mathematics for students of engineering specialties has been built.
Face Super Resolution based on Contrastive Learning

Face super resolution (FSR) is a sub-field of super resolution (SR), which is to reconstruct low resolution (LR) face image into high resolution (HR) face image. Recently, the FSR methods based on face prior have been proved to be effective in FSR on higher upscaling factors. However, existing prior guided methods mostly adopt supervised prior extraction models trained with labels. The performance of supervised prior extraction method mainly depends on the accuracy of label so that the implicit informations of data are not fully utilized. And in practical application, the label acquisition work is routine and laborious. Therefore, to solve these problems, this paper proposes a novel contrastive learning (CL) based FSR method, which is based on the iterative collaboration of image reconstruction network and contrastive learning network. In each iteration, the reconstruction network uses the priors generated by the contrastive learning network to assist the image reconstruction and generates higher-quality SR images. Then, the SR image will feed into contrastive learning network to obtain more accurate prior. In addition, a new contrastive learning constraint function is designed to extract the representation of the augmented facial image as a prior by analysing the principal component information of the image. Quantitative and qualitative experimental results show that the proposed method is superior to the most advanced FSR method in high-quality face images super resolution reconstruction.
Mining Regional Relation from Pixel-wise Annotation for Scene Parsing

Scene parsing is an important and challenging task in computer vision, which assigns semantic labels to each pixel in the entire scene. Existing scene parsing methods only utilize pixel-wise annotation as the supervision of neural network, thus, some similar categories are easy to be misclassified in the complex scenes without the utilization of regional relation. To tackle these above challenging problems, a Regional Relation Network (RRNet) is proposed in this paper, which aims to boost the scene parsing performance by mining regional relation from pixel-wise annotation. Specifically, the pixel-wise annotation is divided into a lot of fixed regions, so that intra- and inter-regional relation are able to be extracted as the supervision of network. We firstly design an intra-regional relation module to predict category distribution in each fixed region, which is helpful for reducing the misclassification phenomenon in regions. Secondly, an inter-regional relation module is proposed to learn the relationships among each region in scene images. With the guideline of relation information extracted from the ground truth, the network is able to learn more discriminative relation representations. To validate our proposed model, we conduct experiments on three typical datasets, including NYU-depth-v2, PASCAL-Context and ADE20k. The achieved competitive results on all three datasets demonstrate the effectiveness of our method.
A Fast and Effective Framework for Camera Calibration in Sport Videos

Computing the relative homography between the sports field template and the corresponding field in a video frame is an important task in camera calibration. In this paper, a fast and effective framework is proposed for addressing this task. The proposed framework has three processing modules. First, a semantic segmentation network is presented to obtain the segmented video frames. Second, a regression network is developed and combined with the direct linear transformation (DLT) algorithm to compute the homography. Third, the enhanced correlation coefficient (ECC) technique is leveraged to refine the estimated homography. The proposed framework is evaluated on 2014 World Cup dataset. The experimental results are compared to the state-of-the-art approaches. The experimental results demonstrate that the accuracy in the proposed framework is superior and the computation speed is competitive.
QR-FLBS: A Personalized Location Big Data Privacy Protection Model

With the rapid development of mobile communication and location-aware device technology, many smart mobile terminal data are generated daily. These data are not only large in quantity and value, but also based on location service, so it is called big location data based on location service. In recent years, large data generated by location-based services has become a research hot spot in the industry. At the same time, people pay more and more attention to the privacy protection of big data. Given the limitations of the traditional privacy protection model based on false location of big location data, this paper proposes a model QR-FLBS (Quick Response-Fingerprint Location-Based Services) based on fingerprint identification and two-dimensional code technology of smart terminals, which can better solve the privacy protection of location services to a certain extent. This experiment adopts the data from real data sets and compares it with the privacy-protection method of false location. The performance of the QR-FLBS model is measured by the response time, robustness and privacy. The experimental results show that the scheme is suitable for the privacy protection of location services and improves privacy protection without sacrificing the quality of user location services.
STSI: Efficiently Mine Spatio- Temporal Semantic Information between Different Multimodal for Video Captioning

As one of the challenging tasks in computer vision, video captioning needs to use natural language to describe the content of video. Video contains complex information, such as semantic information, time information and so on. How to synthesize sentences effectively from rich and different kinds of information is very significant. The existing methods often cannot well integrate the multimodal feature to predict the association between different objects in video. In this paper, we improve the existing encoder-decoder structure and propose a network deeply mining the spatio-temporal correlation between multimodal features. Through the analysis of sentence components, we use spatio-temporal semantic information mining module to fuse the object, 2D and 3D features in both time and space. It is worth mentioning that the word output at the previous time is added as the prediction branch of auxiliary conjunctions. After that, a dynamic gumbel scorer is used to output caption sentences that are more consistent with the facts. The experimental results on two benchmark datasets show that our STSI is superior to the state-of-the-art methods while generating more reasonable and semantic-logical sentences.
One Shot Object Detection Via Hierarchical Adaptive Alignment

Recently, deep learning based object detectors have achieved good performance with abundant labeled data. However, data labeling is often expensive and time-consuming in real life. Therefore, it is required to introduce one shot learning into object detection. In this paper, we propose one shot object detection based on hierarchical adaptive alignment to address the limited information of one shot in feature representation. We present a multi-adaptive alignment framework based on faster R-CNN to extract effective features from query patch and target image using siamese convolutional feature extraction, then generate a fused feature map by aggregating query and target features. We use the fused feature map in object classification and localization. The proposed framework adaptively adjusts feature representation through hierarchical and aggregated alignment so that it can learn correlation between the target image and the query patch. Experimental results demonstrate that the proposed method significantly improves the unseen-class object detection from 24.3 AP50 to 26.2 AP50 on the MS-COCO dataset.