Reviewerly/exHarmony huggingface dataset:
A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval

Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention to multi-label image retrieval. However, in the era of massive remote sensing data, how to increase retrieval efficiency and reduce feature storage while preserving semantic information remains unsolved. Considering the powerful capability of hashing learning in overcoming the curse of dimensionality caused by high-dimensional image representation in Approximate Nearest Neighbor (ANN) search problems, we propose a new semantic-preserving deep hashing model for multi-label remote sensing image retrieval. Our model consists of three main components: (1) a convolutional neural network to extract image features; (2) a hash layer to generate binary codes; (3) a new loss function to better maintain the multi-label semantic information of hash learning contained in context remote sensing image scene. As far as we know, this is the first attempt to apply deep hashing into the multi-label remote sensing image retrieval. Experimental results indicate the effectiveness and promising of the introduction of hashing methods in the multi-label remote sensing image retrieval.
<scp>QuantumPath</scp>: A quantum software development platform

Abstract Quantum computing has experienced a breakthrough. Several companies are taking up the challenge of designing and manufacturing quantum computers, and the supply of tools for quantum software development is growing all the time. This article addresses quantum software development toolkits and introduces the ‘QuantumPath’ platform. In developing QuantumPath, our aim is to fulfil certain principles such as: agnosticism, extensibility, integration, independency, optimisation, scalability, security, usability and software engineering support. This article presents both the architecture itself as well as the main tools that compose QuantumPath, in order to illustrate the support which platform provides to the development and execution of quantum software.
To simulate or not to simulate? Comparing the effectiveness of video‐based training versus virtual reality‐based simulations on interpersonal skills development

Abstract Emerging technologies coupled with the power of big data will transform organizations, requiring drastic changes to traditional approaches in training, communications, and team collaboration, to name only a few examples. Scholars and practitioners in the HRD field are tasked with empirically studying these new phenomena in order to assist both employees and organizations in effectively transitioning to future work. The advent of technological innovations has opened a new frontier of simulated training, including virtual reality‐based simulations (VRBS). But are these innovations more effective than traditional approaches, such as video‐based training (VBT)? Empirical investigations of these emerging technologies have been limited in the field of HRD. The current study addresses the gap in the HRD literature by conducting the first empirical research related to VR technology and its impact on training. The paper examines VRBS with a mixed methods research design within the interpersonal skills training context, comparing the effectiveness of this new medium to more traditional training platforms such as VBT. Results were mixed, with all hypotheses at least partially supported by data. Findings suggest that VR does engage trainees more fully and generate more transferable knowledge than video. The paper triangulates quantitative and qualitative data to draw conclusions and highlight issues that are emerging along with this new technology.
A Lightweight Matrix Factorization for Recommendation With Local Differential Privacy in Big Data

The proliferation of various items recommended by Internet-based systems has resulted in the exponential growth of the number of ratings in the big data era. Recent advances in matrix factorization have made it an effective way to process these ratings for recommendations. However, we confront a challenge in deploying a matrix factorization model for recommendations in big data that arises from the typically resource-constrained local devices regarding their storage space and computing capacity. This paper proposes a novel lightweight matrix factorization for recommendations. Our scheme deploys shard grandients training on user local internet of things(IoT) devices, which makes it possible for users to train big data model locally. We design a two-phase solution to protect the security of users’ data and reduce the dimension of the items. Moreover, we optimize the proposed scheme by introducing a stabilization mechanism to decrease the scale of the perturbed gradients. Some experimental results are given with two real online datasets, MovieLens and LibimSeTi. The theoretical analysis and experimental results demonstrate that compared with other methods, the proposed scheme achieves a good performance in terms of security, accuracy, and efficiency.
An Automated Real-Time Face Mask Detection System Using Transfer Learning with Faster-RCNN in the Era of the COVID-19 Pandemic

Today, due to the pandemic of COVID-19 the entire world is facing a serious health crisis. According to the World Health Organization (WHO), people in public places should wear a face mask to control the rapid transmission of COVID-19. The governmental bodies of different countries imposed that wearing a face mask is compulsory in public places. Therefore, it is very difficult to manually monitor people in overcrowded areas. This research focuses on providing a solution to enforce one of the important preventative measures of COVID-19 in public places, by presenting an automated system that automatically localizes masked and unmasked human faces within an image or video of an area which assist in this outbreak of COVID-19. This paper demonstrates a transfer learning approach with the Faster-RCNN model to detect faces that are masked or unmasked. The proposed framework is built by fine-tuning the state-of-the-art deep learning model, Faster-RCNN, and has been validated on a publicly available dataset named Face Mask Dataset (FMD) and achieving the highest average precision (AP) of 81% and highest average Recall (AR) of 84%. This shows the strong robustness and capabilities of the Faster-RCNN model to detect individuals with masked and un-masked faces. Moreover, this work applies to real-time and can be implemented in any public service area.
Big Data Security Management Countermeasures in the Prevention and Control of Computer Network Crime

This paper aims to study the Countermeasures of big data security management in the prevention and control of computer network crime in the absence of relevant legislation and judicial practice. Starting from the concepts and definitions of computer crime and network crime, this paper puts forward the comparison matrix, investigation and statistics method and characteristic measure of computer crime. Through the methods of crime scene investigation, network investigation and network tracking, this paper studies the big data security management countermeasures in the prevention and control of computer network crime from the perspective of criminology. The experimental results show that the phenomenon of low age is serious, and the number of Teenagers Participating in network crime is on the rise. In all kinds of cases, criminals under the age of 35 account for more than 50%.
Automatic Generation of Chest X-ray Reports Using a Transformer-based Deep Learning Model

Medical images and chest X-rays, in particular, are primarily the most widely used radiological tests in clinical practice for diagnosis and treatment. Reading and interpreting a chest x-ray can be time-consuming for an experienced radiologist, more difficult for the less experienced, and almost impossible for an average person. An X-ray computer-assisted reporting system can ease the physician’s reporting task and provide decision support for radiologists. Also, it will accelerate the deployment of computer-assisted medical decision-making systems. This paper proposes an automatic chest x-ray generating report framework based on combining the transfer learning technique and the transformer approach to generate reports. We apply a pretrained convolutional neural network model from the ImageNet database for feature extraction to exploit deep neural networks’ power. Then, we use a modified transformer encoder-decoder. We test our network on Open I, Indiana University’s Chest X-ray Collection (IU X-Ray), one of the most comprehensive public data sets currently used for chest X-ray captioning. We show that our model produced promising results. Our model performed better than the state-of-the-art results in terms of performance.
SCWC: Structured channel weight sharing to compress convolutional neural networks

Convolutional neural networks (CNNs) have surpassed humans in many computer vision areas. However, the redundancy of CNNs inhibits its application in the embedded device. In this paper, a simple shared channel weight convolution (SCWC) approach is proposed to reduce the number of parameters. Multiplication is much more complex than accumulation, so reducing multiplication is more significant than reducing accumulation in CNNs. The proposed SCWC can reduce the number of multiplications by the distributive property due to the structured channel parameter sharing. Furthermore, the fully trained CNN can be directly compressed without tedious training from scratch. To evaluate the performance of the proposed SCWC, five competitive benchmark datasets for image classification and object detection, CIFAR-10, CIFAR-100, ImageNet, CUB-200–2011 and PASCAL VOC are adopted. Experiments demonstrate that the proposed SCWC can reduce about 50% of parameters and multiplications for ResNet50 with only 0.13% accuracy loss, and reduce about 70% of parameters and multiplications for VGG16 with 1.64% accuracy loss on ImageNet, which is better than many other pruning methods for CNNs. The accuracy loss is very small because of the soft parameter sharing. Moreover, the proposed SCWC is also effective for object detection and fine-grained image classification tasks.
Automated Event Detection and Classification in Soccer: The Potential of Using Multiple Modalities

Detecting events in videos is a complex task, and many different approaches, aimed at a large variety of use-cases, have been proposed in the literature. Most approaches, however, are unimodal and only consider the visual information in the videos. This paper presents and evaluates different approaches based on neural networks where we combine visual features with audio features to detect (spot) and classify events in soccer videos. We employ model fusion to combine different modalities such as video and audio, and test these combinations against different state-of-the-art models on the SoccerNet dataset. The results show that a multimodal approach is beneficial. We also analyze how the tolerance for delays in classification and spotting time, and the tolerance for prediction accuracy, influence the results. Our experiments show that using multiple modalities improves event detection performance for certain types of events.
Hybrid‐360: An adaptive bitrate algorithm for tile‐based 360 video streaming

Abstract In recent years, virtual reality (VR) has become a popular topic. VR videos, as one of the popular applications, can provide the user with an extraordinary and immersed video viewing experience and are gradually entering the public's vision. However, it has faced specific difficulties in the past few years. VR videos need specific collection and displaying equipment. Delivering VR videos needs extra bandwidth and storage space as they contain more information. Traditional adaptive bitrate (ABR) algorithms are mainly designed for 2D videos, and cannot directly apply to VR videos. The delay must be neglectable and there should be fewer stalls. If not, users can suffer from motion sickness. With the development of relevant technologies, those problems can be solved. Using a first‐class VR camera and a head‐mounted display, VR videos can be easily collected and displayed properly. In this work, Hybrid‐360, a novel tile‐based 360 video ABR algorithm, is proposed. Using reinforcement learning, the algorithm can make proper bitrate decisions for every tile in the VR video based on the network and client status. In addition, a system model is proposed to implement the tile‐based 360 ABR algorithm in the real world. Different from traditional 2D videos, VR videos have special characteristics, which brings the need to redesign quality of experience (QoE) model. Additional QoE factors are added into the QoE model, and the weights of every term are adjusted. Using a novel 360 video simulation platform, Hybrid‐360 is then compared with some classical ABR algorithms. It is found to be capable of balancing the video quality as well as video smoothness and fluentness. Also, the algorithm can provide users with a higher QoE.
Principles Based on Social Cues in Multimedia Learning

Social cues can create a sense of partnership between learners and the instructor, which then motivates learners to engage in generative processing. The personalization principle is that people learn better when multimedia lessons are presented in a conversational or polite style, rather than a formal or direct style. The voice principle is that people learn better when multimedia lessons are presented in a human voice rather than computer-generated voice. The image principle is that people do not necessarily learn better when the image of the instructor or a virtual agent is visible on the screen. Finally, the embodiment principle is that people learn better when onscreen pedagogical agents engage in human-like behaviors, such as gesture, eye-contact, or facial expressions.
Evaluation of Fixational Behavior throughout Life

To quantify development of gaze stability throughout life during short and long fixational tasks using eye tracking technology.Two hundred and fifty-nine participants aged between 5 months and 77 years were recruited along the study. All participants underwent a complete ophthalmological assessment. Fixational behavior during long and short fixational tasks was analyzed using a DIVE (Device for an Integral Visual Examination), a digital test assisted with eye tracking technology. The participants were divided into ten groups according to their age. Group 1, 0-2 years; group 2, 2-5 years; group 3, 5-10 years; group 4, 10-20 years; group 5, 20-30 years; group 6, 30-40 years; group 7, 40-50 years; group 8, 50-60 years; group 9, 60-70 years; and group 10, over 70 years.Gaze stability, assessed by logBCEA (log-transformed bivariate contour ellipse area), improved with age from 5 months to 30 years (1.27 vs. 0.57 deg2 for long fixational task, 0.73 vs. -0.04 deg2 for short fixational task), while fixations tend to be longer (1.95 vs. 2.80 msec for long fixational tasks and 0.80 vs. 1.71 msec for short fixational tasks). All fixational outcomes worsened progressively from the fifth decade of life. Log-transformed bivariate contour ellipse area (0.79, 0.83, 0.91, 1.42 deg2 for long fixational task and 0.01, 0.18, 0.28, 0.44 deg2 for short fixational task, for group 7, 8, 9, and 10 respectively). Stimuli features may influence oculomotor performance, with smaller stimuli providing prolonged fixations.Fixational behavior can be accurately assessed from 5 months of age using a DIVE. We report normative data of gaze stability and duration of fixations for every age group. Currently available technology may increase the accuracy of our visual assessments at any age.
NTU-X

The lack of fine-grained joints (facial joints, hand fingers) is a fundamental performance bottleneck for state of the art skeleton action recognition models. Despite this bottleneck, community's efforts seem to be invested only in coming up with novel architectures. To specifically address this bottleneck, we introduce two new pose based human action datasets - NTU60-X and NTU120-X. Our datasets extend the largest existing action recognition dataset, NTU-RGBD. In addition to the 25 body joints for each skeleton as in NTU-RGBD, NTU60-X and NTU120-X dataset includes finger and facial joints, enabling a richer skeleton representation. We appropriately modify the state of the art approaches to enable training using the introduced datasets. Our results demonstrate the effectiveness of these NTU-X datasets in overcoming the aforementioned bottleneck and improve state of the art performance, overall and on previously worst performing action categories. Code and pretrained models can be found at https://github.com/skelemoa/ntu-x.
Discrimination of benign and malignant solid breast masses using deep residual learning-based bimodal computer-aided diagnosis system

One of the most common breast cancer mammographic manifestation is solid mass. If the information obtained from mammography is inadequate, complementary modalities such as ultrasound imaging are used to achieve additional information. Although interest in the combination of information from different modalities is increasing, it is an extremely challenging task. In this regard, a computer-aided diagnosis (CAD) system can be an efficient solution to overcome these difficulties. However, most of the studies have focused on the development of mono-modal CAD systems, and a few existing bimodal ones rely on the extracted hand-crafted features of mammograms and sonograms. In order to meet these challenges, this paper proposes a novel bimodal deep residual learning model. It consists of the following major steps. First, the informative representation for each input image is separately constructed. Second, in order to construct the high-level joint representation of every two input images and effectively explore complementary information among them, the representation layers of them are fused. Third, all of these joint representations are fused to obtain the final common representation of the input images for the mass. Finally, the recognition result is obtained based on information extracted from all input images. The augmentation strategy was applied to enlarge the collected dataset for this study. Best recognition results on the sensitivity, specificity, F1-score, area under ROC curve, and accuracy metrics of 0.898, 0.938, 0.916, 0.964, and 0.917, respectively, are achieved by our model. Extensive experiments demonstrate the effectiveness and superiority of the proposed model over other state-of-the-art models.
Multi-dimensional conditional mutual information with application on the EEG signal analysis for spatial cognitive ability evaluation

This study aims to explore an effective method to evaluate spatial cognitive ability, which can effectively extract and classify the feature of EEG signals collected from subjects participating in the virtual reality (VR) environment; and evaluate the training effect objectively and quantitatively to ensure the objectivity and accuracy of spatial cognition evaluation, according to the classification results. Therefore, a multi-dimensional conditional mutual information (MCMI) method is proposed, which could calculate the coupling strength of two channels considering the influence of other channels. The coupled characteristics of the multi-frequency combination were transformed into multi-spectral images, and the image data were classified employing the convolutional neural networks (CNN) model. The experimental results showed that the multi-spectral image transform features based on MCMI are better in classification than other methods, and among the classification results of six band combinations, the best classification accuracy of Beta1-Beta2-Gamma combination is 98.3%. The MCMI characteristics on the Beta1-Beta2-Gamma band combination can be a biological marker for the evaluation of spatial cognition. The proposed feature extraction method based on MCMI provides a new perspective for spatial cognitive ability assessment and analysis.
Who Authored <i>On Liberty</i>? Stylometric Evidence on Harriet Taylor Mill's Contribution

Abstract It is well known that John Stuart Mill (JSM) repeatedly acknowledges Harriet Taylor Mill's (HTM) substantial contribution to On Liberty . After her death, however, he decides to publish the book under his name only. Are we justified in continuing this practice, initiated by JSM, of refusing unequivocal co-authorship status to HTM? Drawing on stylometric analyses, we make a preliminary case that JSM did not write On Liberty all by himself and that HTM had a hand in formulating it. Drawing on plausible standards for authorship ascription, we further point out that authorship status requires, in addition to a substantial contribution, the approval by all contributors. We discuss potential reasons to assume that HTM did not approve the published version of On Liberty and would have objected to including her name on the title page.
Are Topics Interesting or Not? An LDA-based Topic-graph Probabilistic Model for Web Search Personalization

In this article, we propose a Latent Dirichlet Allocation– (LDA) based topic-graph probabilistic personalization model for Web search. This model represents a user graph in a latent topic graph and simultaneously estimates the probabilities that the user is interested in the topics, as well as the probabilities that the user is not interested in the topics. For a given query issued by the user, the webpages that have higher relevancy to the interested topics are promoted, and the webpages more relevant to the non-interesting topics are penalized. In particular, we simulate a user’s search intent by building two profiles: A positive user profile for the probabilities of the user is interested in the topics and a corresponding negative user profile for the probabilities of being not interested in the the topics. The profiles are estimated based on the user’s search logs. A clicked webpage is assumed to include interesting topics. A skipped (viewed but not clicked) webpage is assumed to cover some non-interesting topics to the user. Such estimations are performed in the latent topic space generated by LDA. Moreover, a new approach is proposed to estimate the correlation between a given query and the user’s search history so as to determine how much personalization should be considered for the query. We compare our proposed models with several strong baselines including state-of-the-art personalization approaches. Experiments conducted on a large-scale real user search log collection illustrate the effectiveness of the proposed models.