Reviewerly/exHarmony huggingface dataset:
“I will defend your right to free speech, provided I agree with you”: How social media users react (or not) to online out‐group aggression

Abstract Social networking sites (SNS) routinely ban aggressive users. Such bans are sometimes perceived as a limitation to the right to free speech. While research has examined SNS users' perceptions of online aggression, little is known about how observers make trade‐offs between free speech and the desire to punish aggression. By focusing on reactions to an SNS ban, this study explores under what circumstances users consider the protection of the right to free speech as more important than the suppression of aggression. We propose a model of moderated mediation that explains under what circumstances online aggression increases the acceptance of a ban. When posts display aggression, the ban is less likely to be perceived as violating free speech and as unfair. Consequently, aggression reduces the likelihood that users will protest through negative word of mouth. Moreover, users protest against an SNS ban only when this affects an in‐group user (rather than an out‐group user). This in‐group bias, however, diminishes when an in‐group aggressor targets a high warmth out‐group user. The study raises managerial implications for the effective management of aggressive interactions on SNS and for the persuasive communication of a decision to ban a user engaging in aggressive behavior.
Shot Boundary Detection Through Multi-stage Deep Convolution Neural Network

Fast and accurate shot segmentation is very important for content-based video analysis. However, existing solutions have not yet achieved the ideal balance of speed and accuracy. In this paper, we propose a multi-stage shot boundary detection framework based on deep CNN for shot segmentation tasks. The process is composed of three stages, which are respectively for candidate boundary detection, abrupt detection and gradual transition detection. At each stage, deep CNN is used to extract image features, which overcomes the disadvantages of hand-craft feature-based methods such as poor scalability and complex calculation. Besides, we also set a variety of constraints to filter as many non-boundaries as possible to improve the processing speed of the model. In gradual transition detection, we introduce a scheme that can infer the gradual position by computing the probability signals of the start, mid and end of the gradual transition. We conduct experiments on ClipShots and the experimental results show that the proposed model achieves better performance on abrupt and gradual transition detection.
A BP Neural Network Based Recommender Framework with Attention Mechanism

Recently, some attempts have been made in introducing deep neural networks (DNNs) to recommender systems for generating more accurate prediction due to the nonlinear representation learning capability of DNNs. However, they inevitably result in high computational and storage costs. Worse still, due to the relatively small number of ratings that can be fed into DNNs, they may easily suffer from the overfitting issue. To tackle these issues, we propose a novel recommendation framework based on Back Propagation (BP) neural network with attention mechanism, namely BPAM++. In particular, the BP neural network is utilized to learn the complex relationship between the target user and his/her neighbors and the complex relationship between the target item and its neighbors. Compared with DNNs, the shallow neural network, i.e., BP neural network, can not only reduce the computational and storage costs, but also alleviate the overfitting issues in DNNs caused by a relatively small number of ratings. In addition, an attention mechanism is designed to capture the global impact of the nearest users of the target user on their nearest target user sets. Extensive experiments conducted on eight benchmark datasets confirm the effectiveness of the proposed model.
Extending epipolar geometry for real-time structured light illumination II: lossless accuracy

Structured light illumination is a process of 3D scanning using a digital projector to project a series of striped patterns that sweep a target surface, and based on the warping of the stripes viewed by a camera, the shape of the target can be reconstructed. In the case of scanning stripes separately in both horizontal and vertical directions, algorithms have been proposed that achieve real-time reconstruction through look-up tables; however, these look-up tables implement the inversion of one traditional lens projection matrix. In this Letter, we propose look-up tables constructed using (1) both views of a camera and a projector and (2) basic arithmetic operations rather than complex matrix operations to significantly reduce the total number of computations used to reconstruct a point cloud. Experiments show that, with the same accuracy, the proposed tables improve the computation speed by a factor of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"> <mml:mn>6.66</mml:mn> <mml:mo>×<!-- × --></mml:mo> </mml:math> , from 31.00 to 206.61 fps.
ClusterGrad: Adaptive Gradient Compression by Clustering in Federated Learning

Recently, Federated Learning (FL) has drawn tremendous attentions due to its ability to protect client's privacy. In FL, clients collaboratively train machine learning models by merely sharing intermediate computations, i.e., gradients of model parameters. However, training a complicated model involves multiple rounds of interactions between clients and the server via the Internet. Consequently, communication is a primary bottleneck of FL attributed to the poor network conditions and the large amount of interchanged computations. To overcome the communication bottleneck, we propose the ClusterGrad algorithm to compress gradients which can considerably reduce the volume of communicated computations. Our design is based on the fact that there is only a small fraction of gradients whose values are far away from the origin in each round of interaction in FL. We first identify these essential gradients that are far away from 0 using the K-means algorithm. These gradient values are approximated by a novel clustering based quantization algorithm. Then, the rest gradients lying close to 0 are approximated with a single value. We can prove that ClusterGrad outperforms the latest FL gradient compression algorithms: Probability Quantization (PQ) and Deep Gradient Compression (DGC). We conduct extensive experiments with the CIFAR-10 datasets which further demonstrate that ClusterGrad can achieve compression ratio (used interchangeably with compression rate) 123 on average in comparison with PQ and DGC with compression ratios 16 and 60 respectively.
Weighted Microscopic Image Reconstruction

Assume that we inspect a specimen represented as a collection of points. The points are typically organized on a grid structure in 2D- or 3D-space, and each point has an associated physical value. The goal of the inspection is to determine these values. Yet, measuring these values directly (by surgical probes) may damage the specimen or is simply impossible. The alternative is to employ aggregate measuring techniques (e.g., CT or MRI), whereby measurements are taken over subsets of points, and the exact values at each point are subsequently extracted by computational methods. In the Minimum Surgical Probing problem (MSP) the inspected specimen is represented by a graph G and a vector \(\ell \in \mathbb {R}^n\) that assigns a value \(\ell _i\) to each vertex i. An aggregate measurement (called probe) centered at vertex i captures its entire neighborhood, i.e., the outcome of a probe centered at i is \(\mathcal{P}_i = \sum _{j \in N(i) \cup \{i\}} \ell _j\) where N(i) is the open neighborhood of vertex i. Bar-Noy et al. [] gave a criterion whether the vector \(\ell \) can be recovered from the collection of probes \(\mathcal{P}= \{\, \mathcal{P}_v \; | \; v \in V(G)\}\) alone. However, there are graphs where \(\mathcal{P}\) is inconclusive, i.e., there are several vectors \(\ell \) that are consistent with \(\mathcal{P}\). In these cases, we are allowed to use surgical probes. A surgical probe at vertex i returns \(\ell _i\). The objective of MSP is to recover \(\ell \) from \(\mathcal{P}\) and G using as few surgical probes as possible.
Rectified non-linear unit for convolution neural network

Abstract Convolution neural network takes in input image, assign importance (learnable weights and biases) to various aspects/objects in the image and be able to differentiate one from the other. At each layer, a linear transformation is carried out on the received data. Aiming for solving non-linear problems, a mandatory unit named activation function is used by neural networks. The activation function greatly influences the success of training deep neural networks. In this paper, a new rectified non linear function unit that we call Rectified non-linear unit (ReNU) is proposed. The presented activation function returns x − log ( x + 1) for positive values, and zero for negative ones. The ReNU multiplies the received gradient by values between 0 and 1 depending on the importance of neurons ( 1 for the biggest neurons and 0 for the smaller ones) unlike the ReLU that returns the same received gradient for all positive values in its back propagation. Using the proposed activation function the CNN performance remarkably raises. The ReNU has been tested on MNIST and CIFAR-10 data sets and compared to the ReLU and ELU activation functions. The experimental results are satisfactory when compared to known activation functions in terms of convergence speed and CNN accuracy.
Bayesian spectral density estimation using P-splines with quantile-based knot placement

This article proposes a Bayesian approach to estimating the spectral density of a stationary time series using a prior based on a mixture of P-spline distributions. Our proposal is motivated by the B-spline Dirichlet process prior of Edwards et al. (Stat Comput 29(1):67–78, 2019. https://doi.org/10.1007/s11222-017-9796-9 ) in combination with Whittle's likelihood and aims at reducing the high computational complexity of its posterior computations. The strength of the B-spline Dirichlet process prior over the Bernstein–Dirichlet process prior of Choudhuri et al. (J Am Stat Assoc 99(468):1050–1059, 2004. https://doi.org/10.1198/016214504000000557 ) lies in its ability to estimate spectral densities with sharp peaks and abrupt changes due to the flexibility of B-splines with variable number and location of knots. Here, we suggest to use P-splines of Eilers and Marx (Stat Sci 11(2):89–121, 1996. https://doi.org/10.1214/ss/1038425655 ) that combine a B-spline basis with a discrete penalty on the basis coefficients. In addition to equidistant knots, a novel strategy for a more expedient placement of knots is proposed that makes use of the information provided by the periodogram about the steepness of the spectral power distribution. We demonstrate in a simulation study and two real case studies that this approach retains the flexibility of the B-splines, achieves similar ability to accurately estimate peaks due to the new data-driven knot allocation scheme but significantly reduces the computational costs.
An approach for bibliographic citation sentiment analysis using deep learning

Sentiment analysis of scientific citations is a novel and remarkable research area. Most of the work on opinion or sentiment analysis has been suggested on social platforms such as Blogs, Twitter, and Facebook. Nevertheless, when it comes to recognizing sentiments from scientific citation papers, investigators used to face difficulties due to the implied and unseen natures of sentiments or opinions. As the citation references are reflected implicitly positive in opinion, famous ranking and indexing prototypes frequently disregard the sentiment existence while citing. Hence, in the proposed framework the paper emphasizes the issue of classifying positive and negative polarity of reference sentiments in scientific research papers. First, the paper scraps the PDF articles from arxiv.org under the computer science group consisting of articles that are comprised of ‘autism’ in their title, then the paper extracted cited references and assigns polarity scores to each cited reference. The paper uses a supervised classifier with a combination of significant feature sets and compared the performance of the models. Experimental results show that a combined CNN-LSTM deep neural network model results in 85% of accuracy while traditional models result in less accuracy.
An improved E-payment protocol based on quantum blind signature without entanglement

E-payment plays an important role in modern daily life, so the security problem of E-payment has been widely concerned by researchers. In a recent paper [Int. J. Theor. Phys. 57, 2657 (2018)], the authors presented a trusted third-party E-payment protocol based on quantum blind signature without entanglement. In this paper, we show that there is a serious security flaw in their E-payment protocol. That is, the partial keys will be disclosed by performing a simple attack strategy. Furthermore, we propose an improved E-payment protocol and analyze its security.
Cytological Images Clustering of Breast Pathologies

The paper analyzes methods of data clustering. Cytological images of breast cancer were investigated, and nuclei feature of cytological images were calculated. The authors have developed a generalized algorithm for clustering cytological images, which includes the definition of the main features, the calculation of the number of clusters and the K-means algorithm. Computer experiments were conducted using Python computer vision libraries.
CLOUDITY: Cloud Supply Chain Framework Design based on JUGO and Blockchain

Supply chain management (SCM) system is a main requirement for manufacturers and companies to cooperate. There are many management techniques to manage supply chains, such as using Excel sheets. However, that technique is ineffective, insecure, and sensitive to human errors. In this paper, we propose CLOUDITY, a cloud-based SCM system using SELAT (Selective Market) and Blockchain system. We modify JUGO architecture to develop SELAT as a connector between users and cloud service providers. Also, we apply the Blockchain concept to make more secure system. CLOUDITY system can solve several cases: resource provisioning, service selection, authentication, and access control. Also, it improves the data security by checking every data changes of the supply chain management system using Blockchain system.
Mamdani FIS Combined With LU Decomposition Method and Two-Level LWT for Image Watermarking Technique

Fuzzy theory and linear algebra have a substantial role in various sciences and the biggest effect was in the share of computer science. In the field of image processing, the process of digital image watermarking represents the inclusion of a secret logo or an image called watermark into a host image to get a watermarked image to guarantee copyright protection or ownership. The watermarking will be acceptable if it achieves specific qualities like imperceptibility and robustness. In this paper, Mamdani fuzzy inference system (FIS) was employed on the original image for obtaining the controller parameter which works on finding a trade-off between imperceptibility and robustness, and on the other hand, is combined with the algebraic matrix decomposition method LU for proposing a digital watermarking technique in which the watermark is included in the LH2 band produced by applying two-level of the lifting wavelet transform (LWT) using the modular arithmetic (mod). The experimental results show that the proposed algorithm resistant to many attacks.
Object Detection in Live Streaming Video Using Deep Learning Approach

Abstract Object detection in a complex scene is of challenge as the image contains numerous features. Object detection is important in surveillance, healthcare, recognition, manufacturing, military and agriculture etc. The traditional approaches are limited in accuracy and mainly focussing on image datasets. With the application of deep learning algorithm, the accuracy can be increased. In this paper, the Convolutional Neural Network (CNN) is introduced to detect the objects in the image frames. The input of video is decomposed into image frame and the particular image frame is considered for processing. The image is preprocessed and the noises are removed for further processing. In the next step, feature extraction process contains edge detection and extracting the meaningful shape and texture in the image. The shape which contains the higher level feature is then feed into convolutional layer. The convolutional layer consist of four parts such as number of filter, filter size, pooling layer and max pooling layer. With the application of activation function Relu, the objects can be identified. Furthermore, the Gradient descent algorithm is applied for optimization to improve the accuracy. The system has been tested with ImageNet and the results are very promising.
An Empirical Study of Cybercrime and Its Preventions

In this modern era of technology, the world is heavily dependent on technology no matter where one goes. Due to our heavy dependency on technology criminals have taken this advantage for their benefit. Cybercrime is quickly becoming one of the fastest rising forms of modern crimes. Cybercrime are well known for the downfall of so many companies, organizations and personal identities. The main intent of this paper is to define cybercrime, various types of cybercriminals and cybercrime affecting the world and its prevention. This paper will also analyse statistical data on various types of cybercrime and its growth in last few years.
Augmented Reality and Autism Spectrum Disorder Rehabilitation: Scoping review

Currently, augmented reality is integrated into several areas (medical, therapeutic, educational, entertainment,). One of these important areas in which the augmented reality has been incorporated in the field of rehabilitation for autism spectrum disorder (ASD) children, but it does not serve the field well. This report will provide an overview of how to use AR technology in the field of autism spectrum disorder (ASD), from an entertainment perspective to develop their communication skills. Where solutions to the problems suggested in previous research will be discussed and attempted to develop them to reach new hypotheses that serve the use of augmented reality AR to better rehabilitate the behavior of autistic children. The report proposes a future hypothesis and a research plan to solve the problem.
A Norm-Creative Method for Co-constructing Personas With Children With Disabilities: Multiphase Design Study

An increase in the demand for child participation in health care requires tools that enable and empower children to be involved in the co-production of their own care. The development of such tools should involve children, but participatory design and research with children have challenges, in particular, when involving children with disabilities where a low level of participation is the norm. Norm-creative and participatory approaches may bring more effective design solutions for this group. "Personas" is a methodology for increasing user perspectives in design and offers representation when users are absent. However, research on participatory persona generation in this context is limited.The objective of this study was to investigate how norm-creative and participatory design approaches can be integrated in a persona generation method to suit children with disabilities in the design of games for health that target this group.The method development involved interview transcripts and image-based workshops. Sixteen children with various disabilities participated in persona generation through co-creation of characters and scenarios. The results from the workshops were validated together with 8 children without disabilities, 1 young adult with a disability, and 1 rehabilitation professional. A qualitative thematic design analysis was iterated throughout the process.The results consisted of an image-based and iterative co-construction method. It was accompanied by examples of personas that were generated and validated within a games for health case. The method showed effectiveness in enabling flexible co-construction and communication. The data resonated with social model perspectives, and the development is discussed in terms of participation levels, salutogenic descriptions of barriers, and norm-creative tradeoffs.The resulting method may influence future design projects toward more inclusiveness and enable increased representation for children with disabilities in research and design. Using this method to its full potential requires a norm-critical awareness as well as extensive facilitation. Suggestions for further research include the application of the method to design processes in similar contexts or user groups.
Calibrating feature maps for deep CNNs

Many performance improvement techniques calibrate the outputs of convolutional layers to improve the performance of convolutional neural networks, e.g., Squeeze-and-Excitation Networks (SENets). These techniques train the network to extract calibration weights from the input itself. However, these methods increase the complexity of the model in order to perform calibration. We propose an approach to calibrate the outputs of convolutional layers efficiently. Specifically, we propose an architectural block called Accuracy Booster, which calibrates the convolutional layer outputs channel-wise while introducing minimal extra parameters and computation. We experimentally show that our approach achieves higher performance than existing calibration methods over several datasets and architectures while introducing lesser parameters than them. We also generalize our proposed block to calibrate the channel, width, and height of the layer output in parallel. We empirically show that this type of composite calibration performs better than applying channel-wise calibration, spatial calibration, or both. We validate our approach on the CIFAR-10/100, CUB, ImageNet, and MS-COCO datasets for various tasks. The ResNet-50 architecture with the accuracy booster block performs comparably on the classification task to the ResNet-152 architecture, which has more than twice the number of parameters. We empirically show that our method generalizes well to other tasks such as object detection. We perform extensive ablation experiments to validate our approach.
Method for Generating Captions for Clothing Images to Support Visually Impaired People

Visually impaired people are able to access the shape and texture of objects and recognize them by the sense of touch of hands. However, visual information such as color and pattern of clothing cannot be accessed. Therefore, it is difficult for visually impaired people to coordinate clothes without the assistance of a sighted person. In this study, we aim to develop a system to support visually impaired people to choose clothing. In this paper, we describe a method for converting visual information acquired from clothing into verbal representation using Deep Neural Networks (DNNs). In the experiment, we have developed a caption generating model using our dataset. As a result of try the computer experiment to generate caption for 100 images, we have observed subjectively acceptable captions for about 80% of the results.