Reviewerly/exHarmony huggingface dataset:
Mask wearing detection based on YOLOv3

Abstract The CoVID-19 is still raging around the world, and the work of relying on manpower to detect masks in public places is time-consuming and laborious. To solve this problem, this paper proposes an improved Face_mask Net detection method for convolutional neural network based on YOLOv3. First, a new convolutional neural network Face_mask Net is designed based on YOLOv3 network structure. Second, before training, Face_mask Net uses the K-Means algorithm to cluster the labeled dataset and change its anchor value. Finally, the Face_mask Net loss function uses DIoU and the classifier uses DIoU-NMS. The combination of the two can further improve the detection accuracy of the target. To verify the effectiveness of the proposed algorithm, the Face_mask Dataset for mask detection was collected and annotated in this paper. The experimental results show that Face_mask Net can effectively detect the target of wearing mask and not wearing mask, and its performance can be close to real-time and the accuracy is higher than that of the network before the improvement.
Survey of treemap layout algorithms

This paper provides an overview of published treemap layout algorithms from 1991 to 2019 that were used for information visualization and computational geometry. First, a terminology is outlined for the precise communication of tree-structured data and layouting processes. Second, an overview and classification of layout algorithms is presented and application areas are discussed. Third, the use-case-specific adaption process is outlined and discussed. This overview targets practitioners and researchers by providing a starting point for own research, visualization design, and applications.
DR-KFS: A Differentiable Visual Similarity Metric for 3D Shape Reconstruction

We introduce a differential visual similarity metric to train deep neural networks for 3D reconstruction, aimed at improving reconstruction quality. The metric compares two 3D shapes by measuring distances between multi-view images differentiably rendered from the shapes. Importantly, the image-space distance is also differentiable and measures visual similarity, rather than pixel-wise distortion. Specifically, the similarity is defined by mean-squared errors over HardNet features computed from probabilistic keypoint maps of the compared images. Our differential visual shape similarity metric can be easily plugged into various 3D reconstruction networks, replacing their distortion-based losses, such as Chamfer or Earth Mover distances, so as to optimize the network weights to produce reconstructions with better structural fidelity and visual quality. We demonstrate this both objectively, using well-known shape metrics for retrieval and classification tasks that are independent from our new metric, and subjectively through a perceptual study.
General image encryption algorithm based on deep learning compressed sensing and compound chaotic system

Many image compression and encryption algorithms based on traditional compressed sensing and chaotic systems are time-consuming, have low reconstruction quality, and are suitable only for grayscale images. In this paper, we propose a general image compression encryption algorithm based on a deep learning compressed sensing and compound chaotic system, which is suitable for grayscale images and RGB format color images. Color images can be directly compressed and encrypted, but grayscale images need copying from 1 channel to 3 channels. First, the original image is divided into multiple 3 × 33 × 33 non-overlapping image blocks and the bilinear interpolation Bilinear and convolutional neural network are used to compress the image, so that the compression network has no restriction on the sampling rate and can obtain high-quality compression of image. Then a composite chaotic system composed of a two-dimensional cloud model and Logistic is used to encrypt and decrypt the compressed image (sliding scrambling and vector decomposition), and finally the decrypted image is reconstructed. In the reconstruction network, the convolutional neural network and bilinear interpolation Bilinear are mainly responsible for reconstructing the contour structure information, and the fully connected layer is mainly responsible for reconstructing and combining the color information to reconstruct a high-quality image. For grayscale images, we also need to calculate the average value of the corresponding positions of the 3 channels of the reconstructed image, and change the 3 channels into 1 channel. The experimental results show that the general image encryption algorithm based on deep learning compressed sensing and compound chaos system has great advantages in data processing quality and computational complexity. Although in the network the color images are used for training, the quality of grayscale image reconstruction is still better than that of other algorithms. The image encryption algorithm has a large enough key space and associates the plaintext hash value with the key, which realizes the encryption effect of one image corresponding to one key, thus being able to effectively resist brute force attacks and selective plaintext attacks. Compared with it in the comparison literature, the correlation coefficient is close to an ideal value, and the information entropy and the clear text sensitivity index are also within a critical range, which enhances the confidentiality of the image.
Deep medical image analysis with representation learning and neuromorphic computing

Deep learning is increasingly used in medical imaging, improving many steps of the processing chain, from acquisition to segmentation and anomaly detection to outcome prediction. Yet significant challenges remain: (i) image-based diagnosis depends on the spatial relationships between local patterns, something convolution and pooling often do not capture adequately; (ii) data augmentation, the de facto method for learning three-dimensional pose invariance, requires exponentially many points to achieve robust improvement; (iii) labelled medical images are much less abundant than unlabelled ones, especially for heterogeneous pathological cases; and (iv) scanning technologies such as magnetic resonance imaging can be slow and costly, generally without online learning abilities to focus on regions of clinical interest. To address these challenges, novel algorithmic and hardware approaches are needed for deep learning to reach its full potential in medical imaging.
Predicting Canine Posture With Smart Camera Networks Powered by the Artificial Intelligence of Things

In today's society, the number of people rearing pets has increased and their awareness of the need to protect pets' health has increased. Pet posture behaviour analysis and prediction are providing assistance in the medical treatment of pets. Hence, the demand for pet skeleton drawing applications has risen dramatically. Our proposed system predicts pet posture using smart camera networks powered by the artificial intelligence of things. This system is built on a platform using a Raspberry Pi embedded system. The system can determine from an image whether there is a detection target and generate a contour mask based on Mask R-CNN Technology. According to object detection, poses and key parts can be identified to predict and draw pet skeletons. Simultaneously, the behavioural action of a pet can be determined according to continuous skeleton data and then the system will actively inform the owner to perform subsequent processing.
Image encryption algorithm based on new five-dimensional multi-ring multi-wing hyperchaotic system

The complex structure of hyperchaos and its complex dynamic behavior have a good application prospect in the fields of image encryption, digital watermarking and information security. Therefore, it has become very important to generate chaotic attractors with multi-vortex and multi-winged multi-rings with complex topologies. In this paper, we propose a new five-dimensional hyperchaotic system capable of generating multi-ring and multi-wing, and carry out theoretical analysis and numerical simulation experiments on some basic dynamic characteristics of the chaotic system. Such as equilibrium point, dissipation, Lyapunov exponent, bifurcation diagram, phase diagram and so on. In the process of encryption, first, we decompose the plaintext image matrix and the five chaotic sequences into an orthogonal matrix and an upper triangular matrix by QR decomposition. The five chaotic sequences generated by the chaotic system are respectively decomposed into an upper triangular matrix and a lower triangular matrix by the LU decomposition method. The upper triangular matrix decomposed by the QR decomposition method and the lower triangular matrix decomposed by the LU decomposition method are respectively added to obtain five discrete chaotic sequences. At the same time, the five discrete chaotic sequences are added to the upper triangular matrix decomposed by the LU decomposition method to obtain the final five discrete chaotic sequences. Secondly, the orthogonal matrix decomposed by the plaintext image matrix is multiplied by five orthogonal matrices decomposed by five chaotic sequences. At the same time, the elements in the upper triangular matrix decomposed by the plaintext image matrix are chaotically arranged by the chaotic sequence, and then the two matrices after the operation are multiplied. Finally, the multiplied matrix is chaotically placed on the bit by a chaotic sequence. Then use the chaotic sequence to perform a bitwise XOR operation to obtain the final encrypted image. The theoretical analysis and simulation results show that the algorithm has large key space and strong key sensitivity. It can effectively resist the attacks of statistical analysis and gray value analysis, and has good encryption effect on digital image encryption. This image encryption algorithm using a combination of conventional encryption and chaotic encryption does not have a defined plaintext ciphertext mapping relationship.
Multichannel Steganography in Digital Images for Multiple Receivers

In this article, we focus on a new concept called multichannel steganography, in which a sender is able to transmit different secret data to multiple receivers via the same cover image. First, we propose a general framework to achieve multichannel steganography based on modern steganographic paradigm. Then, we design two schemes to, respectively, achieve separable and sequential multichannel steganography with the proposed framework. In the separable scheme, a receiver can extract secret data for him/her using the corresponding data hiding key. For other parts of secret data, the receiver cannot affirm the existence and, therefore, cannot extract them. In the sequential scheme, the multiple receivers are dependent. The receiver with the highest priority can extract data first. After that, the receiver with the second priority can extract data when authorized by the highest priority receiver, and so on. In addition, the two schemes achieve multichannel steganography without decreasing the undetectability of steganography.
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction

Multi-scale representations deeply learned via convolutional neural networks have shown tremendous importance for various pixel-level prediction problems. In this paper we present a novel approach that advances the state of the art on pixel-level prediction in a fundamental aspect, i.e. structured multi-scale features learning and fusion. In contrast to previous works directly considering multi-scale feature maps obtained from the inner layers of a primary CNN architecture, and simply fusing the features with weighted averaging or concatenation, we propose a probabilistic graph attention network structure based on a novel Attention-Gated Conditional Random Fields (AG-CRFs) model for learning and fusing multi-scale representations in a principled manner. In order to further improve the learning capacity of the network structure, we propose to exploit feature dependant conditional kernels within the deep probabilistic framework. Extensive experiments are conducted on four publicly available datasets (i.e. BSDS500, NYUD-V2, KITTI and Pascal-Context) and on three challenging pixel-wise prediction problems involving both discrete and continuous labels (i.e. monocular depth estimation, object contour prediction and semantic segmentation). Quantitative and qualitative results demonstrate the effectiveness of the proposed latent AG-CRF model and the overall probabilistic graph attention network with feature conditional kernels for structured feature learning and pixel-wise prediction.
Multi-Fading Factor and Updated Monitoring Strategy Adaptive Kalman Filter-Based Variational Bayesian

Aiming at the problem that the performance of adaptive Kalman filter estimation will be affected when the statistical characteristics of the process and measurement of the noise matrices are inaccurate and time-varying in the linear Gaussian state-space model, an algorithm of multi-fading factor and an updated monitoring strategy adaptive Kalman filter-based variational Bayesian is proposed. Inverse Wishart distribution is selected as the measurement noise model and the system state vector and measurement noise covariance matrix are estimated with the variational Bayesian method. The process noise covariance matrix is estimated by the maximum a posteriori principle, and the updated monitoring strategy with adjustment factors is used to maintain the positive semi-definite of the updated matrix. The above optimal estimation results are introduced as time-varying parameters into the multiple fading factors to improve the estimation accuracy of the one-step state predicted covariance matrix. The application of the proposed algorithm in target tracking is simulated. The results show that compared with the current filters, the proposed filtering algorithm has better accuracy and convergence performance, and realizes the simultaneous estimation of inaccurate time-varying process and measurement noise covariance matrices.
Spam E-mail Classification Recurrent Neural Networks for Spam E-mail Classification on an Agglutinative Language

In this study, we have provided an alternative solution to spam and legitimate email classification problem.The different deep learning architectures are applied on two feature selection methods, including the Mutual Information (MI) and Weighted Mutual Information (WMI).Firstly, feature selection methods including WMI and MI are applied to reduce number of selected terms.Secondly, the feature vectors are constructed with concept of the bag-of-words (BoW) model.Finally, the performance of system is analyzed with using Artificial Neural Network (ANN), Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (BILSTM) models.After experimental simulations, we have observed that there is a competition between detection results of using WMI and MI when commented with accuracy rates for the agglutinative language, namely Turkish.The experimental scores show that the LSTM and BILSTM give 100% accuracy scores when combined with MI or WMI, for spam and legitimate emails.However, for particular crossvalidation, the performance WMI is higher than MI features in terms e-mail grouping.It turns out that WMI and MI with deep learning architectures seem more robust to spam email detection when considering the high detection scores.
Fast incremental expectation maximization for finite-sum optimization: nonasymptotic convergence

Fast incremental expectation maximization (FIEM) is a version of the EM framework for large datasets. In this paper, we first recast FIEM and other incremental EM type algorithms in the Stochastic Approximation within EM framework. Then, we provide nonasymptotic bounds for the convergence in expectation as a function of the number of examples n and of the maximal number of iterations $$K_\mathrm {max}$$ . We propose two strategies for achieving an $$\epsilon $$ -approximate stationary point, respectively with $$K_\mathrm {max}= O(n^{2/3}/\epsilon )$$ and $$K_\mathrm {max}= O(\sqrt{n}/\epsilon ^{3/2})$$ , both strategies relying on a random termination rule before $$K_\mathrm {max}$$ and on a constant step size in the Stochastic Approximation step. Our bounds provide some improvements on the literature. First, they allow $$K_\mathrm {max}$$ to scale as $$\sqrt{n}$$ which is better than $$n^{2/3}$$ which was the best rate obtained so far; it is at the cost of a larger dependence upon the tolerance $$\epsilon $$ , thus making this control relevant for small to medium accuracy with respect to the number of examples n. Second, for the $$n^{2/3}$$ -rate, the numerical illustrations show that thanks to an optimized choice of the step size and of the bounds in terms of quantities characterizing the optimization problem at hand, our results design a less conservative choice of the step size and provide a better control of the convergence in expectation.
Prioritizing the Issues extracted for Getting Right People on Right Project in Software Project Management From Vendors’ Perspective

Software project management inspires and urges the spirit of software developing team members which continues until project completion. Obviously, success of every project based on right selection of team members that ensures to meet the desired requirements of any software developing project. The fundamental aim of current study is to extract and prioritize issues faced by vendors of global software development (GSD) organizations during the selection of right team with having aim to complete the project successfully. As a methodology, a systematic literature review (SLR) used for data extraction and categorization, a questionnaire survey adopted for data validation, and a hierarchical analytical process (AHP) used for prioritizing extracted findings. A total of 12 issues are extracted and grouped into 3 categories (association, teamwork, and fascination). The overall result showed that “association” is the most important category as compare to other categories. Similarly, communication and coordination issues, team's consistency and stability issues, and lack of expertise issues, etc are highlighted as the most critical issues during selection of right people for the right project from vendors' perspective.
Agency and Extraction in Emerging Industrial Drone Applications

Rapidly diffusing 'industry 4.0' technologies stand to impact a broad range of stakeholders. Prior to implementation, forward looking formative analyses can identify systems and policy designs to promote equitable benefit. We investigate this potential through an analysis of stakeholders to a potential drone implementation on a small commercial farm in Rwanda. Translating stakeholders' imaginaries within a post-colonial frame, we identify hopes and concerns related to agency and influenced by global and local systems of power. The findings highlight constraints that recommend system designs promoting local agency and control and policies designed to balance local data management against potentially 'extractive' multinational data transfer processes.
A Method to Assess and Argue for Practical Significance in Software Engineering

A key goal of empirical research in software engineering is to assess practical significance, which answers the question whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case study's data was previously analyzed (Afzal <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">et al.</i> , 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.
Operation strategy under additional service and refurbishing effort in online second-hand market

As consumption has increased, the abundance of many second-hand goods has become a thorny problem for consumers, while the improper disposal of used goods poses a considerable threat to the environment. To achieve better recycling of second-hand goods, an increasing number of second-hand trading platform owners have emerged. In this paper, we develop game-theoretic models to study how different operation modes affect the supply chain members’ profits, price-to-performance ratios for consumers, the environment and social welfare. Three types of operation modes are proposed depending on the pricing power and the provider of the service: consignment, resale, and sales-plus-service. We find that under certain conditions, the resale and sales-plus-service modes can yield the greatest profits for the e-trading platform owner and the second-hand product supplier. The consignment mode may not bring the optimal profit to the second-hand product supplier but might offer consumers the best price-to-performance ratio even if the e-trading platform owner’s reputation is low. We also show that the mode is not the best way to sell second-hand goods from the perspective of the environment and that the government should encourage highly reputable e-trading platform owners to undertake sales and services. Moreover, an e-trading platform owner service cost may improve social welfare in the sales-plus-service mode.
Representation Learning via Invariant Causal Mechanisms

Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks with data augmentations and have achieved significant success, but our theoretical understanding of this success remains limited. In this paper we analyze self-supervised representation learning using a causal framework. We show how data augmentations can be more effectively utilized through explicit invariance constraints on the proxy classifiers employed during pretraining. Based on this, we propose a novel self-supervised objective, Representation Learning via Invariant Causal Mechanisms (ReLIC), that enforces invariant prediction of proxy targets across augmentations through an invariance regularizer which yields improved generalization guarantees. Further, using causality we generalize contrastive learning, a particular kind of self-supervised method, and provide an alternative theoretical explanation for the success of these methods. Empirically, ReLIC significantly outperforms competing methods in terms of robustness and out-of-distribution generalization on ImageNet, while also significantly outperforming these methods on Atari achieving above human-level performance on 51 out of 57 games.