Reviewerly/exHarmony huggingface dataset:
A Survey of Deep Learning Region Proposal and Background Recognition Techniques for Moving Object Detection

Object detection is the technique of locating targets in an image scene. Moving object detection is to track the object in successive frames which forms the video. Moving object detection forms the primary step in surveillance applications and traffic monitoring systems. With the advent of deep learning remarkable performance is observed in object detection. But the following challenges of detecting small and low-resolution moving objects in the region of interest (ROI), objects with infrequent motion still remain. Deep learning background detection techniques aim at cancelling global camera motion and background movements. The review focuses on the strengths and weaknesses of various deep learning-based moving target region proposal networks and background separation methods.
Automated Real-Time Recognition of Non-emotional Conversational Head-Gestures for Social Robots

Social robotics promises to augment human caretakers in the medical industry, elderly care industry, entertainment industry, education industry and space and deep-sea explorations in the future. Automated understanding of human facial expression, speech, non-emotional conversational gestures and their integration are necessary for human-robot interaction. Conversational gestures comprise conversational head-gestures, hand-gestures, gaze, lip movements, and their synchronous integration with speech. In this paper, we implement a synchronous colored Petri net model for automated recognition of non-emotional conversational head-gestures. The scheme integrates head-motion analysis, eye-focus analysis, and their synchronization. The technique performs video analysis to derive x and y coordinates of facial feature points, stillness-vector, and silence-vector in real-time. These vectors are analyzed to derive a signature comprising meta-attribute values of the corresponding synchronous Petri net graph for each gesture. These signatures are matched against archived signatures to recognize and label the actual gestures in real-time. An algorithm using dynamic matrix-based implementation has been presented. Conversational head-gestures have been partitioned into multiple classes based upon the combination of type of head-motions, eye-focus, repeated motion, and associated speech to reduce ambiguities in gesture-labeling caused by sensor inaccuracies, sampling interval choices and various threshold limitations. A confusion matrix for a subset of gestures shows that signatures and classification on major attributes achieve a high percentage of recall in gesture recognition.
Academic libraries and research data management: a case study of Dataverse global adoption

Purpose The purpose of this study is to examine the development of Dataverse, a global research data management consortium. The authors examine specifically the institutional characteristics, the utilization of the associated data sets and the relevant research data management services at its participating university libraries. This evidence-based approach is essential for understanding the current state of research data management practices in the global context. Design/methodology/approach The data was collected from 67 participants’ data portals between December 1, 2020, and January 31, 2021. Findings Over 80% of its current participants joined the group in the past five years, 2016–2020. Thirty-three Dataverse portals have had less than 10,000 total downloads since their inception. Twenty-nine participating universities are included in three major global university ranking systems, and 18 of those university libraries offer research data services. Originality/value This project is an explorative study on Dataverse, an international research data management consortium. The findings contribute to the understanding of the current development of the Dataverse project as well as the practices at the participating institutions. Moreover, they offer insights to other global higher education institutions and research organizations regarding research data management. While this study is practical, its findings and observations could be of use to future researchers interested in developing a framework for data work in academic libraries.
Deep Q-Learning Network with Bayesian-Based Supervised Expert Learning

Deep reinforcement learning (DRL) algorithms interact with the environment and have achieved considerable success in several decision-making problems. However, DRL requires a significant number of data before it can achieve adequate performance. Moreover, it might have limited applicability when DRL agents are able to learn in a real-world environment. Therefore, some algorithms combine DRL agents with supervised learning and leverage previous additional knowledge. Some have integrated a deep Q-learning network with a behavioral cloning model that can exploit supervised learning as prior learning. The algorithm proposed in this study is also based on these methods and is intended to update the loss function of the existing technique into a Bayesian approach. The supervised loss function used in existing algorithms and the loss function based on the Bayesian method proposed in this study differ in terms of the utilization of prior knowledge. Using prior knowledge and not using prior knowledge, such as the cross entropy being symmetric. As a result of the various OpenAI Gym environments, such as Cart-Pole and MountainCar, the learning convergence performance was improved. In particular, the proposed method can be applied to achieve fairly stable learning during the early stage when learning in a sparse environment is uncertain.
Daily Estimation of Global Solar Irradiation and Temperatures Using Artificial Neural Networks through the Virtual Weather Station Concept in Castilla and León, Spain

In this article, the interpolation of daily data of global solar irradiation, and the maximum, average, and minimum temperatures were measured. These measurements were carried out in the agrometeorological stations belonging to the Agro-climatic Information System for Irrigation (SIAR, in Spanish) of the Region of Castilla and León, in Spain, through the concept of Virtual Weather Station (VWS), which is implemented with Artificial Neural Networks (ANNs). This is serving to estimate data in every point of the territory, according to their geographic coordinates (i.e., longitude and latitude). The ANNs of the Multilayer Feed-Forward Perceptron (MLP) used are daily trained, along with data recorded in 53 agro-meteorological stations, and where the validation of the results is conducted in the station of Tordesillas (Valladolid). The ANN models for daily interpolation were tested with one, two, three, and four neurons in the hidden layer, over a period of 15 days (from 1 to 15 June 2020), with a root mean square error (RMSE, MJ/m2) of 1.23, 1.38, 1.31, and 1.04, respectively, regarding the daily global solar irradiation. The interpolation of ambient temperature also performed well when applying the VWS concept, with an RMSE (°C) of 0.68 for the maximum temperature with an ANN of four hidden neurons, 0.58 for the average temperature with three hidden neurons, and 0.83 for the minimum temperature with four hidden neurons.
Color Transfer Algorithm between Images Based on a Two-Stage Convolutional Neural Network

A color transfer algorithm between images based on two-stage convolutional neural network (CNN) is proposed. The first stage network is based on VGG19 architecture as the backbone. The reference image-based color transfer (RICT) model was used to extract the features of the reference image and the target image, so as to realize the color transfer between them. The second stage is based on progressive convolutional neural network (PCNN) as its backbone. The palette-based emotional color enhancement (PECE) model is adopted to enhance the emotional coloring of the resulting image by comparing the palette, emotional value and the proportion of each color of the reference image. Through five sets of experiments, it is proved that the visual effect processed by our model is obviously better than several main colorization methods in both subjective evaluation and objective data. It can be applied to various complex scenes, and in the near future, it can also be better applied to the fields of digital restoration of old image archives, medical image coloring, art restoration, remote sensing image enhancement, infrared image enhancement and other fields.
An Augmented Reality Serious Game for Learning Intelligent Wheelchair Control: Comparing Configuration and Tracking Methods

This work proposes an augmented reality serious game (ARSG) for supporting individuals with motor disabilities while controlling robotic wheelchairs. A racing track was used as the game narrative; this included restriction areas, static and dynamic virtual objects, as well as obstacles and signs. To experience the game, a prior configuration of the environment, made through a smartphone or a computer, was required. Furthermore, a visualization tool was developed to exhibit user performance while using the ARSG. Two user studies were conducted with 10 and 20 participants, respectively, to compare (1) how different devices enable configuring the ARSG, and (2) different tracking capabilities, i.e., methods used to place virtual content on the real-world environment while the user interacts with the game and controls the wheelchair in the physical space: C1-motion tracking using cloud anchors; C2-offline motion tracking. Results suggest that configuring the environment with the computer is more efficient and accurate, in contrast to the smartphone, which is characterized as more engaging. In addition, condition C1 stood out as more accurate and robust, while condition C2 appeared to be easier to use.
Sim-to-Real Deep Reinforcement Learning for Safe End-to-End Planning of Aerial Robots

In this study, a novel end-to-end path planning algorithm based on deep reinforcement learning is proposed for aerial robots deployed in dense environments. The learning agent finds an obstacle-free way around the provided rough, global path by only depending on the observations from a forward-facing depth camera. A novel deep reinforcement learning framework is proposed to train the end-to-end policy with the capability of safely avoiding obstacles. The Webots open-source robot simulator is utilized for training the policy, introducing highly randomized environmental configurations for better generalization. The training is performed without dynamics calculations through randomized position updates to minimize the amount of data processed. The trained policy is first comprehensively evaluated in simulations involving physical dynamics and software-in-the-loop flight control. The proposed method is proven to have a 38% and 50% higher success rate compared to both deep reinforcement learning-based and artificial potential field-based baselines, respectively. The generalization capability of the method is verified in simulation-to-real transfer without further training. Real-time experiments are conducted with several trials in two different scenarios, showing a 50% higher success rate of the proposed method compared to the deep reinforcement learning-based baseline.
Low SNR Multi-Emitter Signal Sorting and Recognition Method Based on Low-Order Cyclic Statistics CWD Time-Frequency Images and the YOLOv5 Deep Learning Model

It is difficult for traditional signal-recognition methods to effectively classify and identify multiple emitter signals in a low SNR environment. This paper proposes a multi-emitter signal-feature-sorting and recognition method based on low-order cyclic statistics CWD time-frequency images and the YOLOv5 deep network model, which can quickly dissociate, label, and sort the multi-emitter signal features in the time-frequency domain under a low SNR environment. First, the denoised signal is extracted based on the low-order cyclic statistics of the typical modulation types of radiation source signals. Second, the time-frequency graph of multisource signals was obtained through CWD time-frequency analysis. The cyclic frequency was controlled to balance the noise suppression effect and operation time to achieve noise suppression of multisource signals at a low SNR. Finally, the YOLOv5s deep network model is used as a classifier to sort and identify the received signals from multiple radiation sources. The method proposed in this paper has high real-time performance. It can identify the radiation source signals of different modulation types with high accuracy under the condition of a low SNR.
Multi-scale adaptive weighted network for polarization computational imaging super-resolution

Due to the pixel limit of the polarization imaging detector and the object detection conditions, the spatial resolution of the object polarization image in actual imaging detection applications is generally low. Convolutional neural networks (CNNs) have been introduced to image super-resolution (SR). However, these methods rarely explore the internal connections between local spatial components and multi-scale feature maps in different receptive fields. To this end, we propose a multi-scale adaptive weighted network (MSAWN) for polarization computational imaging super-resolution to gain superior reconstruction performance. Computational imaging methods centered on information acquisition and interpretation can obtain high-resolution images that are superior to imaging systems. Specifically, we use a limited amount of memory and computational power even with multi-scale and multi-level polarization information. Second, a spatial pyramid structure based on the space-channel attention mechanism is designed to effectively adjust the feature weight of polarization information. Third, we adopt an adaptive weight unit to reduce redundant network branches and parameters. Particularly, we design an innovative reconstruction layer with inputs coming from multiple paths by means of sub-pixel convolution. The experimental results show that the proposed method achieves better reconstruction accuracy and visual effect, and the objective evaluation indexes such as PSNR and SSIM are significantly improved.
Multi‐task few‐shot learning with composed data augmentation for image classification

Abstract Few‐shot learning (FSL) attempts to learn and optimise the model from a few examples on image classification, which is still threatened by data scarcity. To generate more data as supplements, data augmentation is considered as a powerful and popular technique to enhance the robustness of few‐shot models. However, there are still some weaknesses in applying augmentation methods. For example, all augmented samples have similar semantic information with respect to different augmented transformations, which makes these traditional augmentation methods incapable of learning the property being varied. To address this challenge, we introduce multi‐task learning to learn a primary few‐shot classification task and an auxiliary self‐supervised task, simultaneously. The self‐supervised task can learn transformation property as auxiliary self‐supervision signals to improve the performance of the primary few‐shot classification task. Additionally, we propose a simple, flexible, and effective mechanism for decision fusion to further improve the reliability of the classifier, named model‐agnostic ensemble inference (MAEI). Specifically, the MAEI mechanism can eliminate the influence of outliers for FSL using non‐maximum suppression. Extensive experiment results demonstrate that our method can outperform other state‐of‐the‐art methods by large margins.
A weighted distance-based approach with boosted decision trees for label ranking

Label Ranking (LR) is an emerging non-standard supervised classification problem with practical applications in different research fields. The Label Ranking task aims at building preference models that learn to order a finite set of labels based on a set of predictor features. One of the most successful approaches to tackling the LR problem consists of using decision tree ensemble models, such as bagging, random forest, and boosting. However, these approaches, coming from the classical unweighted rank correlation measures, are not sensitive to label importance. Nevertheless, in many settings, failing to predict the ranking position of a highly relevant label should be considered more serious than failing to predict a negligible one. Moreover, an efficient classifier should be able to take into account the similarity between the elements to be ranked. The main contribution of this paper is to formulate, for the first time, a more flexible label ranking ensemble model which encodes the similarity structure and a measure of the individual label importance. Precisely, the proposed method consists of three item-weighted versions of the AdaBoost boosting algorithm for label ranking. The predictive performance of our proposal is investigated both through simulations and applications to three real datasets.
Residual UNet with Dual Attention—An ensemble residual <scp>UNet</scp> with dual attention for multi‐modal and multi‐class brain <scp>MRI</scp> segmentation

Abstract In this paper, we have proposed a variant of UNet for brain magnetic resonance imaging (MRI) segmentation. The proposed model, termed as Residual UNet with Dual Attention (RUDA), addresses the two significant challenges of UNet: extraction of the complex features with unclear boundaries and the problem of over‐segmentation due to the redundancy caused by the skip connection usage. RUDA is constituted upon the residual blocks for extracting the complex structures. It Introduces attention into the skip connections to avoid redundancy and thereby the chance of over‐segmentation. Our model segments brain MRI into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) regions, which are considered crucial informative substructures for diagnosing neurological disorders such as Alzheimer's. It has been implemented in an ensemble manner to accommodate the multi‐sequence (T1‐weighted, IR, and T2‐FLAIR) scans. The empirical analysis shows that with an accuracy of 93.80%, RUDA outperforms the two baseline models: UNet (91.37%), ResUNet (91.44%).
A perspective trend of hyperelliptic curve cryptosystem for lighted weighted environments

In modern cryptography, Light Weighted Environments (LWE) refer to low-constrained environments (Pelzl et al., 2003) such as smartphones and other electronic devices (Ullah et al., 2020). There is a significant effect on user-to-user, user-to-server, server-to-user, and other forms of inter-communication due to the LWE. The researcher employs many cryptographic techniques, including encryption, decryption, signcryption, and digital signature, to heighten the effect of information security in LWE scenarios. The challenging task of the proposed techniques, such as RSA, Discrete Logarithm Problem (DLP), Elliptic Curves (EC), etc., is unsuitable for LWE. Due to the large key size, it does not provide an efficient solution in terms of computation and communication costs. Therefore, the researchers use the concept of the Hyper Elliptic Curve (HEC), which is more suitable for LWE. In this paper, we analyze the concept of HEC for resource-constrained environments. The HEC perspective trend is used to ensure the maintenance of the security of LWE and to provide effective performance in computation and communication overheads.
Perception and Use of <scp>COVID</scp> Contact Tracing Mobile Applications in New York State (<scp>NYS</scp>)

The study explores the use of COVID-19 related apps for contact tracing deployed in New York State (NYS). The project seeks to understand potential differences in perception, adoption, or privacy concerns among racial and ethnic populations and across age groups. Using the Antecedent-Privacy Concerns-Outcomes (APCO) framework and the perceived usefulness construct, this study explores factors influencing the individual level adoption of these apps. Data collected from 120 Amazon Mechanical Turkers located in NYS was analyzed. The results indicate that race and gender are important factors to consider in expanding the Antecedent-Privacy Concerns-Outcomes (APCO) framework. Specifically, race impacted the perception of the seriousness of the pandemic, with Asians and Black being serious about the pandemic. Age played a role in privacy and security concerns. The youngest group of respondents, aged 18-24, did not have many privacy and security concerns about mobile apps. These results provided empirical results and evidence that can contribute to the expansion of the APCO model and help further the model's development.
Sharing Research Design, Methods and Process Information in and out of Academia

Abstract An interview study of (N=) 16 senior researchers at a major Canadian research university shows that researchers use a broad range of means to share information about research process, methods and design to different audiences. The purpose of sharing information on these aspects of research is to enable redoing and replicating earlier studies, to preserve knowledge of how studies were conducted, to understand data, and because of the social pressure to share. Time as a barrier and distance to overcome, disciplinary and contextual variation have a major impact on sharing. In the light of the findings, a one size fits all approach is unlikely to succeed. It is critical to choose appropriate methods that help to focus on what is relevant to share in particular disciplinary contexts, and for specific audiences and goals of transparency.
Fall detection based on interpretation of important features with wrist-wearable sensors

Smartwatches are attracting great industrial attention, and the demand for daily monitoring is increasing. Thus, a method for high fall detection with the smallest data is required to minimize the load on telecommunication. It is difficult to run machine learning on a smartwatch for fall detection, and the data must be transmitted to a mobile phone. Therefore, we propose a method for detecting a fall with few features by detecting important features in the inertial signal acquired from the wristband sensor. We used the FallAllD dataset for evaluation and detected 60 descriptive statistical features from each of the three-axis acceleration and angular velocity. Additionally, important features were detected through SHapley additive exPlanations. The sampling frequency of the inertial signal obtained the highest accuracy of 94% at 120 Hz, with at least 14 important features.
Survey of Approaches and Techniques for Security Verification of Computer Systems

This article surveys the landscape of security verification approaches and techniques for computer systems at various levels: from a software-application level all the way to the physical hardware level. Different existing projects are compared, based on the tools used and security aspects being examined. Since many systems require both hardware and software components to work together to provide the system’s promised security protections, it is not sufficient to verify just the software levels or just the hardware levels in a mutually exclusive fashion. This survey especially highlights system levels that are verified by the different existing projects and presents to the readers the state of the art in hardware and software system security verification. Few approaches come close to providing full-system verification, and there is still much room for improvement.