Reviewerly/exHarmony huggingface dataset:
On the Role of Depth Predictions for 3D Human Pose Estimation

Following the successful application of deep convolutional neural networks to 2D human pose estimation, the next logical problem to solve is static 3D human pose estimation from monocular images. While previous solutions have shown some success, they do not fully utilize the depth information from the 2D inputs. With the goal of addressing this depth ambiguity, we build a system that takes 2D joint locations as input along with their estimated depth value and predicts their 3D positions in camera coordinates. Our system out performs comparable frame-by-frame 3D human pose estimation networks on the largest publicly available 3d motion data set, Human 3.6M. To provide further evidence for the usefulness of predicted depth values in the 3D pose estimation problem, we perform an extensive statistical analysis showing that even with potentially noisy depth predictions there is still a statistically significant correlation between the predicted depth value and the true depth value.
Multi-task model for human pose estimation and person detection

Human pose estimation and person detection are two fundamental tasks of human behavior analysis. There has been remarkable progress in these two tasks separately since the development of convolutional neural network. Recently, researchers have paid more attention to one-stage human pose estimation and person detection for the needs of practical application. However, few researches have been reported on completing these two tasks in a single network simultaneously. There are two main reasons: (1) designing an effective mechanism that makes full use of their relevance and complementation to achieve common progress, especially the pose estimation accuracy is really challenging, (2) evaluation bias caused by scale sensitivity difference remains unsolved. To address these problems, we propose a multi-task model for human pose estimation and person detection simultaneously, named PersonPD (person pose and person detection). It predicts keypoint heatmaps and regresses a 4D relative displacement vector (l,t,r,b) which actually encodes the person bounding box and also acts as keypoints' grouping clues. A maximum IOU matching algorithm, named IOU-grouping, is presented to group body joints into individual persons. At the same time, it generates accurate person detection results. In this simple but effective method, our model get competitive person detection and pose estimation performance on COCO datasets<sup>1</sup>.
DIRA: disjoint-identity resolution adaptation for low-resolution face recognition

Low-resolution face recognition (LRFR) intends to identify unknown poor-quality face images and is widely employed in real-world surveillance applications. While collecting a large-scale labeled low-resolution (LR) face dataset could be conducive, it is practically infeasible due to labor costs and privacy issues. In contrast, accessing high-resolution (HR) face datasets is relatively effortless. However, prevailing domain adaptation techniques are often tenuous as they demand sharing of similar face images at different resolutions. We propose disjoint-identity resolution adaptation (DIRA) to transfer substantial face semantic representations from HR to LR face images, despite <i>disjoint identities</i> and limited labeled LR images. We accredit that continuous adversarial learning between HR-LR resolution alignment and segregation renders effective feature extraction and discriminative LR face representation. Our experimental results show a notable performance boost over the recent state-of-the-art methods for the challenging realistic low-resolution face recognition task.
A foreground detection based video stabilization method and its application in aerospace measurement and control

The output video of the optical equipment in the aerospace measurement and control field is prone to the problem of image quality degradation caused by the operator’s unstable manual operation. to improve the classical motion estimation based video stabilization algorithm, a novel video stabilization method based on foreground detection is proposed in this paper. Firstly, a object detection datasets based on historical images of the launch center is collected and labeled. Secondly, inspired by transfer learning and prior knowledge of the image in launch center, a YOLO-based object detection method for rocket launching scene is designed. Then, the object detection method is introduced into the motion estimation based video stabilization pipeline in which the object detection is used for foreground detection so the tracked feature points are filtered to reduce the global motion estimation error caused by the motion of the background area. Thus, the error stabilization problem in the classic motion estimation-based video stabilization method is avoided. Experiments show that the video stabilization method proposed in this paper achieved better image stabilization effect in subject and object evaluation. This paper has certain reference significance for exploring the application of deep learning and artificial intelligence technology in the field of aerospace measurement and control field.
Face tampering detection based on spatiotemporal attention residual network

Fake technology has evolved to the point where fake faces are increasingly difficult to distinguish from real ones. If the forged face videos spread wildly on social media, social unrest or personal reputation damage may lead to social unrest. A face tampering detection method (RALNet) with spatiotemporal attention residual network is designed to reduce the misuse of face data due to malicious dissemination. Firstly, we propose a process to extract video face data, which reduces the interference of irrelevant information and improves the utilization of data processing. Then, based on the characteristics of incoherence and inconsistency in spatial and temporal information of tampered videos, the spatial domain features and temporal domain features of the target face video are extracted by introducing an attention mechanism of residual network and long short-term memory network to classify the targets as true or fake. The experimental results show that the method can effectively detect whether the face data is tampered, and its detection accuracy is better than other methods. In addition, it also achieves good performance in terms of recall, precision, and F1 score.
Ship target detection based on multitask learning

Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.
Image Edge Analysis and Application Based on Least Squares Fitting Model

This paper presents an effective method to automatically segment and fit the edge contour curve data of a picture into straight line segments, circular arc segments or elliptical arc segments. Firstly, the curvature of each point is calculated, and the curvature change breakpoints are found to distinguish between straight lines, circular arcs and elliptical arcs; then, circular arc segments and elliptical arcs are distinguished by the change degree of curvature of adjacent points, and the automatic segmentation of contour edges is realized. Finally, the circular arc segment and elliptic arc segment are fitted by least squares method, based on which the length of the straight line, the direction angle of the circular arc, the rotation angle of the elliptic arc and other related information are calculated. The results show that our model can better identify the feature texture information of image edges.
LSTM-SENet: category decoding model with an attention mechanism

With the development of deep neural networks (DNN), building visual decoding models based on functional magnetic resonance imaging (fMRI) to simulate the visual system of the human brain and studying visual mechanisms have become a research hotspot. Although existing visual decoding models built using DNNs have achieved a certain accuracy, most models ignore the differences between different voxels. Among them, the BRNN-based category decoding model uses the bidirectional long short term memory (LSTM) network to simulate the visual bidirectional information flow, which improves the decoding accuracy, but it uses the voxels of each brain area as an overall input model. Therefore, we embed the channel attention module, the Squeeze-and-Excitation Networks (SENet), into the LSTM network to construct an LSTM-SENet vision that introduces an attention mechanism The decoding model allows the model to learn by itself and assign different weights to each voxel, focusing on important voxels, thereby improving the classification accuracy of natural images. The experimental results show that our method improves the accuracy of (three-level) category decoding than other methods, and the results further verify the effectiveness of building a visual decoding model based on the visual mechanism.
Toward a classification of the supercharacter theories of <i>C</i><sub><i>p</i></sub> × <i>C</i><sub><i>p</i></sub>

Abstract In this paper, we study the supercharacter theories of elementary abelian $p$ -groups of order $p^{2}$ . We show that the supercharacter theories that arise from the direct product construction and the $\ast$ -product construction can be obtained from automorphisms. We also prove that any supercharacter theory of an elementary abelian $p$ -group of order $p^{2}$ that has a non-identity superclass of size $1$ or a non-principal linear supercharacter must come from either a $\ast$ -product or a direct product. Although we are unable to prove results for general primes, we do compute all of the supercharacter theories when $p = 2,\, 3,\, 5$ , and based on these computations along with particular computations for larger primes, we make several conjectures for a general prime $p$ .
Fast Retrieval Method of Portal Information Based on a Chaotic Genetic Algorithm

The traditional retrieval method cannot respond to the influence of the change in the portal website’s information characteristics, resulting in low efficiency. In this regard, a fast information retrieval method based on a chaotic genetic algorithm is proposed. According to the relevant theory of association rules, the correlation between information data of dynamic portal websites is calculated; different portal website information is retrieved based on the Markov model output; a chaotic genetic algorithm is used to fuse different portal website information. The information data constructs a decision tree for rapid retrieval of portal information, uses the vector form to express the characteristics of portal information, and finally realizes the rapid retrieval of portal information. The experimental results show that the designed method takes up to 15 ms when the sample complexity is high, which shows that the designed method has high efficiency and is of great significance in practical applications.
An Assessment of Character building in light of National Professional Standards of Public Sector Teachers at Primary Level in District Peshawar

The main objective of the study was to assess the teaching practices of the public sector primary school teachers in light of the national professional standards (NPS) approved by the Ministry of Education (MoE) on 23rd February 2009 in collaboration with the financial support of the USAID. The program was named as STEP which stands for strengthening Teacher Education Program. The study was delimited to only 3rd standard, being related to character building. For data collection, a criteria based Rubric was constructed. Data was collected from 360 public sector primary school teachers of District Peshawar. Collected Data, for selected standard, was analyzed through mean score. Findings of the study showed that majority of the teachers didn’t perform as per investigated Standard. So, it was recommended that Education department should chalk out a widespread program for the training and supervision of public sector primary school teachers regarding Character building of students.
Collaborative Governance of Rural Relative Poverty under Blockchain and Back Propagation Neural Network

It aims at exploring the effect of coordinated relative poverty governance in rural areas based on blockchain and back propagation (BP) neural network, so that the living standard of rural areas can be significantly improved. In view of the shortcomings and defects existing in the rural relative poverty governance, artificial intelligence technologies such as blockchain and neural network are innovatively introduced to conduct intelligent storage and data visualization analysis on the rural relative poverty governance process. A visualized governance and mechanism innovation model of rural relative poverty data based on blockchain and BP neural network is constructed. Finally, its performance is analyzed through experiments. The results show that the accuracy of data visualization prediction of the proposed algorithm model reaches 94.31%. When the amount of data is 5.5 Mb, the calculation consumption of the proposed algorithm is 0.08 s, and the average leakage rate finally stabilizes below 10%. Therefore, the constructed poverty data visualization governance model can achieve good data transmission security performance while ensuring high classification accuracy. It can provide experimental basis and contribution for the intelligent development and mechanism innovation of rural relative poverty governance.
FemTech.dk Research Initiative

Abstract FemTech.dk is situated in the Department of Computer Science at University of Copenhagen Denmark and has been an ongoing inquiry into the specific circumstances within computer science that produce gender imbalance and includes activities dedicated to making a change through design interventions. FemTech.dk was created in 2016 to engage with research within gender and diversity and to explore the role of gender equity as part of digital technology design and development. FemTech.dk considers how and why computer science as a field and profession in Denmark has such a distinct unbalanced gender representation in the twenty-first century. The focus was initially on the student base of the bachelor’s program in computer science, which from the 1980s until 2016 was remarkably smaller than for other science programs at the University of Copenhagen. In terms of numbers, only 15 women students entered the bachelor’s degree program in 2012 and 2013, and only 12 women students entered the program in 2014. In each of these 3 years, more than 160 students entered the program in total. Reviewing the 15-year period 2000–2014, the share of women students in the program was 7–9%, the lowest percentage of women in a study program across all of the University of Copenhagen. To compare, in 2016 the share of women students in the Math program was 30%, and in Physics was 25%.
Interventionist Research

Abstract FemTech.dk is fundamentally about combining research and interventions with a focus on making long-term change. Inspired by sociotechnical design (Mumford 2006) and action research (Bjørn and Boulus 2011; Bjørn and Boulus-Rødje 2015), FemTech.dk follows two main interlinked paths: (1) unpacking and understanding the challenges related to unbalanced gender representation in computer science, and (2) intervening and extending the field of computer science to allow for multiple, diverse agendas. In this way, the overall methodological approach is characterized as action research.
Application of Factor Analysis in Image Modeling Design of Environmental Protection Gold Stove

In order to design an environmentally friendly gold furnace that considers both traditional cultural elements and modern decorative elements, this paper puts forward the application of factor analysis in the image modeling design of an environmentally friendly gold furnace. Firstly, the elements of lines, colors, and patterns were extracted by factor analysis. By using the hierarchical structure model and factor analysis, the synchronous parameter fusion model of image modeling design factors of the environmental protection gold furnace is constructed to fuse the output reliability parameters of image modeling of the environmental protection gold furnace and adjust them adaptively. The factor analysis and process control of image modeling of environmental protection golden stoves are carried out using the method of differential feature analysis. Then, the Atlas and image Kanban are analyzed, and the image design factors of the environmental protection golden stove are excavated. Finally, the concepts of cultural space and cultural time are introduced. Based on the study of historical and material materials, the overall comparative analysis is carried out from three aspects: shape, color, and pattern. Combined with the extracted target image and the excavated design factors, the image modeling design of the environmental protection gold furnace is effectively carried out, providing design methods and sample references for the innovative design of the environmental protection gold furnace. The experimental results show that the mining design factors can effectively analyze the problems existing in the modeling design of the current environmental protection gold furnace, with a low bit error rate and strong practicability.
Modified Fisher Discriminant Analysis, An Application on Facial Detection

Facial detection task is considered as one of the most important applications of computer vision. Precise feature extraction and efficient classification are required for optimal results. Several approaches have been proposed to address this problem, including unsupervised and supervised technic like SVM. In this work we try to implement FDA into facial detection in combination with Gabor Features, and further to propose a modification using pseudo inverse to improve it and compare it to other classification technic in a facial detection system. The obtained results show that our modified FDA outperforms other algorithms in detection while maintaining unbeatable speed of FDA.
Distance Based Image Classification: A solution to generative classification’s conundrum?

Most classifiers rely on discriminative boundaries that separate instances of each class from everything else. We argue that discriminative boundaries are counter-intuitive as they define semantics by what-they-are-not; and should be replaced by generative classifiers which define semantics by what-they-are. Unfortunately, generative classifiers are significantly less accurate. This may be caused by the tendency of generative models to focus on easy to model semantic generative factors and ignore non-semantic factors that are important but difficult to model. We propose a new generative model in which semantic factors are accommodated by shell theory’s Wen-Yan et al. (IEEE Trans Pattern Anal Mach Intell, 2021) hierarchical generative process and non-semantic factors by an instance specific noise term. We use the model to develop a classification scheme which suppresses the impact of noise while preserving semantic cues. The result is a surprisingly accurate generative classifier, that takes the form of a modified nearest-neighbor algorithm; we term it distance classification. Unlike discriminative classifiers, a distance classifier: defines semantics by what-they-are; is amenable to incremental updates; and scales well with the number of classes.
An improved approach for two-stage detection model

With the rapid development of deep learning models, the performance of object detection have made great success in recent years. However, the problem of low detection efficiency still exists in two-stage detection model. In this paper, we design a lightweight fully convolution neural network(LFCNN) as backbone to extract features more efficiently. Firstly, LFCNN is a lightweight network with only a small number of network parameters, which ensures that it can complete the feature extraction task more quickly while maintaining detection accuracy. Secondly, LFCNN uses residual connection to ensure the performance of the deep network and uses dense connection to realize the reuse and fusion of multi-layer features of the network, which significantly improve the detection accuracy. Moreover, we also come up with a novel method called anchor scale generator(ASG) to obtain the proper predefined anchor scales for generating more accurate region proposals, which further enhances localization ability of objects. A large number of experiments on Pascal VOC and COCO datasets show that our approach is superior to other methods in both bounding boxes localization accuracy and detection performance.
To sharpen or not to sharpen? On the effect of sharpening filters applied to magnified images

Super-resolution algorithms aim to produce magnified high-resolution versions from low-resolution images. Some methods, however, are prone to generate blur during the process. Simple sharpening filters are adopted to alleviate this type of artifact. However, the actual effectiveness of this approach is not clear-cut in the literature. This work evaluates the effect of three simple sharpening filters on the quality of images obtained from super-resolution methods. Two metrics were considered in the evaluation: the Peak signal-to-noise ratio (PSNR) metric, and the Learned Perceptual Image Patch Similarity (LPIPS). One of the filters could consistently improve the LPIPS metric of magnified images from diverse benchmark sets on top of seven super-resolution methods. The increments obtained for the perceptual metric seem to occur due to the sharpening effect. Improvements on PSNR values were not as consistent.
A new zero-watermarking algorithm based on deep learning

A new zero-watermarking algorithm based on deep learning is proposed to improve the robustness of the zero-watermarking, in which zero-watermarking image generation and copyright verification are both completed using neural networks. First, a stylized image is generated from a host image and a logo image with a time stamp through VGG network. Then, the stylized image is encrypted by the Arnold transform and registered as a zero-watermarking image in Intellectual Property Protection (IPR). Finally, the RCNN network is designed to extract the logo image to verify the copyright of host images. The experimental results show that the security and robustness of the algorithm are better than the existing zero-watermarking algorithm.