Reviewerly/exHarmony huggingface dataset:
A Development Challenge: Embracing Technology in Leadership Development

Leaders must train in technology use and build a portfolio of competencies that mark a rapidly emerging future. Three categories of technology with examples of how to approach increasing technology literacy in each are presented. Five emerging or rapidly advancing technology trends provide a framework for leader development. [ J Contin Educ Nurs . 2021; 52(6):260–262.]
A New Unsupervised Learning Method for 3D Deformable Medical Image Registration

Accurate registration of medical images is vital for doctor's diagnosis and quantitative analysis. In this paper, we propose a new unsupervised learning network SA-VoxelMorph for 3D deformable medical image registration. In this paper, we design a novel network architecture SAU-Net by introducing a new Binary Spatial Attention Module (BSAM) into skip connection of 3D U-Net, which can make full use of the spatial information extracted from the encoding and corresponding decoding stage. Moreover, from variational method, control function can better control the generation of registration field φ. Therefore, we also propose a new registration loss function with novel smoothing term based on optimal control method to generate better φ. We verify our method on two datasets including ADNI and PPMI, and obtain excellent results on magnetic resonance image (MRI) registration with higher average Dice scores and better diffeomorphic registration fields compared with other state-of-the-art methods. The experimental results show the method can achieve better performance in brain MRI registration.
Improving Web API Usage Logging

A Web API (WAPI) is a type of API whose interaction with its consumers is done through the Internet. While being accessed through the Internet can be challenging, mostly when WAPIs evolve, it gives providers the possibility to monitor their usage. Currently, WAPI usage is mostly logged for traffic monitoring and troubleshooting. Even though they contain invaluable information regarding consumers’ behavior, they are not sufficiently used by providers. In this paper, we first consider two phases of the application development lifecycle, and based on them we distinguish two different types of usage logs, namely development logs and production logs. For each of them we show the potential analyses (e.g., WAPI usability evaluation) that can be performed, as well as the main impediments, that may be caused by the unsuitable log format. We then conduct a case study using logs of the same WAPI from different deployments and different formats, to demonstrate the occurrence of these impediments and at the same time the importance of a proper log format. Next, based on the case study results, we present the main quality issues of WAPI logs and explain their impact on data analyses. For each of them, we give some practical suggestions on how to deal with them, as well as mitigating their root cause.
An Intelligent Image Processing-based Approach to Optimize Vehicle Headlamp Aiming

In this paper, a novel image processing-based approach is proposed to optimize vehicle headlamps aiming. Currently, most aiming devices rely on numerical derivative oriented methods to find the essential focal features, also called reference points, to perform the aiming process. However, these approaches are not robust, and minor changes in isocurves' smoothness may result in finding inaccurate aiming focal features. To address the associated robustness issue, a statistical signal processing-based approach named penalized contrast for changepoint detection is proposed. Experimental results indicate a high accuracy level of the proposed method concerning the master-tuned ground truth case studies while suggesting a robust mathematical process. Furthermore, it provides an extra level of flexibility for defining alternative focal features by calculating several points along isocurves, each of which can be considered as an axillary reference point for enhancing the aiming process.
Practical Experience of Teaching Economic Disciplines Through Game Methods

The article is devoted to the role of game methods for students of generation " Z " as a tool for increasing the activation of modern learning. The research is based on the analysis of Russian and foreign publications that reveal the importance of game methods of teaching students of economic disciplines in higher education. The main purpose of the research is to substantiate the theoretical provisions of game methods in teaching and to transfer the practical experience of their application in teaching economic disciplines. The role of toys for children's development in the historical aspect is determined. &#x0D; The author's experience of developing scenarios of classes in the form of a game of teaching economic modules, including accounting disciplines, using innovative methods and emphasizing the need to apply such methods in the educational process is proposed. Observing the basic principles of teaching: through practical experience, consistency and visibility, such pedagogical goals as increasing the involvement of students in the educational process, the formation of new professional skills by practicing them through game methods are achieved. &#x0D; The main conclusions are that by revealing the projects (developments) of individual game methods, the author shows the possibility of increasing the contingent for economic specialties and, as a result, the interest of students in the chosen profession. The author proposes the definition of "game methods of economic training" as a modeled set of activities in the form of a game, aimed at obtaining professional competencies related to economic activity. The conclusions can be used in teaching and in the development of work programs for special disciplines of students of economic directions in FGOST 3++.
Utilization of mixed reality tools in the design of wireless assistive products

The focus of this paper is on the use of mixed reality tools, specifically augmented reality (AR) andtangible augmented reality (TAR), for evaluating design concepts and soliciting feedback in the design of assistive products. AR and TAR are tools that allow a person to look at an environment where computer-generated elements are dynamically shown alongside real objects. In the context of product development, it allows a 3D model of a product (such as an appliance on a countertop) to be viewed within a real environment. Additionally, it can allow a user to a user to move, operate and interact with the virtual product. Technologies such as AR and TAR have become more accessible to regular users as computing power has become smaller and more powerful. While use by consumers is still somewhat limited, AR is already finding uses in design. This paper will present the current challenges that must be addressed related to using AR including implementation and use of AR by designers, the level of validity in representing a product to users with disabilities, and ability of users to use the technology to provide useful/accurate feedback on a concept compared to manufactured physical prototypes.
EMIP Toolkit: A Python Library for Customized Post-processing of the Eye Movements in Programming Dataset

The use of eye tracking in the study of program comprehension in software engineering allows researchers to gain a better understanding of the strategies and processes applied by programmers. Despite the large number of eye tracking studies in software engineering, very few datasets are publicly available. The existence of the large Eye Movements in Programming Dataset (EMIP) opens the door for new studies and makes reproducibility of existing research easier. In this paper, a Python library (the EMIP Toolkit) for customized post-processing of the EMIP dataset is presented. The toolkit is specifically designed to make using the EMIP dataset easier and more accessible. It implements features for fixation detection and correction, trial visualization, source code lexical data enrichment, and mapping fixation data over areas of interest. In addition to the toolkit, a filtered token-level dataset with scored recording quality is presented for all Java trials (accounting for 95.8% of the data) in the EMIP dataset.
Bayesian Pseudoinverse Learners: From Uncertainty to Deterministic Learning

Pseudo-inverse learners (PILs) are a kind of feedforward neural network trained with the pseudoinverse learning algorithm, which can be traced back to 1995 originally. PIL is an approach for nongradient descent learning, and its main advantage is the lower computational cost and fast learning procedure, which is especially relevant in the edge computing research field. However, PIL is mostly applied to a deterministic learning problem, while in the real world, the greatest case that is of concern is the uncertainty learning problem. In this work, under the framework of the synergetic learning system (SLS), we introduce an approximated synergetic learning scheme, which can transform uncertainty learning into deterministic learning. We call this new learning framework the Bayesian PIL, and the advantages are also demonstrated in this work.
Simple Linear Iterative Clustering (SLIC) and Graph Theory-Based Image Segmentation

With the extensive application of deep acquisition devices, it has become more feasible to access deep data. The accuracy of image segmentation can be improved by depth data as an additional feature. The current research interests in simple linear iterative clustering (SLIC) are because it is a simple and efficient superpixel segmentation method, and it is initially applied for optical images. This mainly comprises three operation steps (i.e., initialization, local k-means clustering, and postprocessing). A scheme to develop the image over-segmentation task is introduced in this chapter. It considers the pixels of an image with simple linear iterative clustering and graph theory-based algorithm. In this regard, the main contribution is to provide a method for extracting superpixels with greater adherence to the edges of the regions. The experimental tests will consider biomedical grayscales. The robustness and effectiveness will be verified by quantitative and qualitative results.
Local learning from municipal ransomware attacks: A geographically weighted analysis

The prevalence of security threats like ransomware continues to increase and victimize a wide range of targets, which includes municipal information systems. These attacks are commonly reported in media outlets available in attacked communities. This study seeks to understand how effective news reporting can be toward influencing the behavior of people who live within the proximity of the attack. The results suggest a geographic influence on individual behaviors that could well extend beyond the context of information security into other areas of behavioral IS research.
Impact of translation difficulty and working memory capacity on processing of translation units: evidence from Chinese-to-English translation

This study aims to examine how the source text’s (ST) translation difficulty and subject’s working memory (WM) capacity impact the number and size of translation units (TUs). Based on the data collected through key-logging of 38 students translating two relatively easy and difficult texts, the number of production TUs (PTU) at all levels and sizes of average production TU (APTU) were calculated and compared. It was found that while translating the difficult text, the subjects used more 1-to-6-word TUs, but their APTU sizes were not clearly reduced. The number of PTUs at each level did not vary with WM capacity in the translation of either the easy or the difficult text, but the APTU size of subjects with high WM capacity was larger than that of those with low WM capacity. However, in the translation of the difficult text, the size of APTU of subjects with high WM capacity was significantly reduced compared to those with low WM capacity, thus neutralizing the difference between the two groups in the translation of the relatively easy text.
A Multi-source Unsupervised Domain Adaptation Method for Wearable Sensor based Human Activity Recognition

Human Activity Recognition (HAR) refers to recognizing a human's ongoing actions through sensor data. At present, one of the main problems faced by Human Activity Recognition is that different subjects, devices and wearing positions can cause inconsistent sensor data distribution. When a classification model trained using some labeled dataset is used to classify a new unlabeled data with different distributions, there will be a significant performance loss. However, it is difficult to annotate manually sensor data for new subjects. Prior works applying unsupervised domain adaptation methods to solve this problem only used a single source domain. However, in practice, it is common to have multiple labeled source domains. Inspired by a work in the field of computer vision, we propose an unsupervised domain adaptation method for human activity recognition using multiple source domains. Experimental results on a commonly used public HAR dataset show that our model can effectively alleviate the performance loss caused by inconsistent distributions. Moreover, compared with the single-source domain adaptation, the multi-source domain adaptation method can improve the accuracy further.
Real-time social distance alerting and contact tracing using image processing

Social distancing has been enforced by many government and health organizations as an imperative measure to stop the spread of the ongoing COVID-19 crisis. However, current surveillance systems do not have the ability to detect breaches in social distancing nor real-time contact tracing. This chapter aims to provide a novel solution to this problem with the help of image processing techniques. The proposed system will generate structured data involving social distancing breaches and face mask detection based on surveillance footages. In the case of a person testing positive for the virus, it will also project the susceptible victims who have been in contact with the person through contact tracing, An installation of this kind will decrease the spread substantially and enable real-time contact tracing.
Learning Multi-Modal Volumetric Prostate Registration With Weak Inter-Subject Spatial Correspondence

Recent studies demonstrated the eligibility of convolutional neural networks (CNNs) for solving the image registration problem. CNNs enable faster transformation estimation and greater generalization capability needed for better support during medical interventions. Conventional fully-supervised training requires a lot of high-quality ground truth data such as voxel-to-voxel transformations, which typically are attained in a too tedious and error-prone manner. In our work, we use weakly-supervised learning, which optimizes the model indirectly only via segmentation masks that are a more accessible ground truth than the deformation fields. Concerning the weak supervision, we investigate two segmentation similarity measures: multiscale Dice similarity coefficient (mDSC) and the similarity between segmentation-derived signed distance maps (SDMs). We show that the combination of mDSC and SDM similarity measures results in a more accurate and natural transformation pattern together with a stronger gradient coverage. Furthermore, we introduce an auxiliary input to the neural network for the prior information about the prostate location in the MR sequence, which mostly is available preoperatively. This approach significantly outperforms the standard two-input models. With weakly labelled MR-TRUS prostate data, we showed registration quality comparable to the state-of-the-art deep learning-based method.
Challenges for Optical Flow Estimates in Elastography

In this paper, we consider visualization of displacement fields via optical flow methods in elastographic experiments consisting of a static compression of a sample. We propose an elastographic optical flow method (EOFM) which takes into account experimental constraints, such as appropriate boundary conditions, the use of speckle information, as well as the inclusion of structural information derived from knowledge of the background material. We present numerical results based on both simulated and experimental data from an elastography experiment in order to demonstrate the relevance of our proposed approach.
Characterizing the Performance of Deep Neural Networks for Eye-Tracking

Deep neural networks (DNNs) provide powerful tools to identify and track features of interest, and have recently come into use for eye-tracking. Here, we test the ability of a DNN to predict keypoints localizing the eyelid and pupil under the types of challenging image variability that occur in mobile eye-tracking. We simulate varying degrees of perturbation for five common sources of image variation in mobile eye-tracking: rotations, blur, exposure, reflection, and compression artifacts. To compare the relative performance decrease across domains in a common space of image variation, we used features derived from a DNN (ResNet50) to compute the distance of each perturbed video from the videos used to train our DNN. We found that increasing cosine distance from the training distribution was associated with monotonic decreases in model performance in all domains. These results suggest ways to optimize the selection of diverse images for model training.
Online statistical inference for parameters estimation with linear-equality constraints

Stochastic gradient descent (SGD) and projected stochastic gradient descent (PSGD) are scalable algorithms to compute model parameters in unconstrained and constrained optimization problems. In comparison with SGD, PSGD forces its iterative values into the constrained parameter space via projection. From a statistical point of view, this paper studies the limiting distribution of PSGD-based estimate when the true parameters satisfy some linear-equality constraints. Our theoretical findings reveal the role of projection played in the uncertainty of the PSGD-based estimate. As a byproduct, we propose an online hypothesis testing procedure to test the linear-equality constraints. Simulation studies on synthetic data and an application to a real-world dataset confirm our theory.
Learning Discriminative Features Using Multi-label Dual Space

Multi-label learning handles instances associated with multiple class labels. The original label space is a logical matrix with entries from the Boolean domain \(\in \left\{ 0,1 \right\} \). Logical labels are not able to show the relative importance of each semantic label to the instances. The vast majority of existing methods map the input features to the label space using linear projections with taking into consideration the label dependencies using logical label matrix. However, the discriminative features are learned using one-way projection from the feature representation of an instance into a logical label space. Given that there is no manifold in the learning space of logical labels, which limits the potential of learned models. In this work, inspired from a real-world example in image annotation to reconstruct an image from the label importance and feature weights. We propose a novel method in multi-label learning to learn the projection matrix from the feature space to semantic label space and projects it back to the original feature space using encoder-decoder deep learning architecture. The key intuition which guides our method is that the discriminative features are identified due to map the features back and forth using two linear projections. To the best of our knowledge, this is one of the first attempts to study the ability to reconstruct the original features from the label manifold in multi-label learning. We show that the learned projection matrix identifies a subset of discriminative features across multiple semantic labels. Extensive experiments on real-world datasets show the superiority of the proposed method.
Tracking Active Observers in 3D Visuo-Cognitive Tasks

Most past and present research in computer vision involves passively observed data. Humans, however, are active observers in real life; they explore, search, select what and how to look. In this work, we present a psychophysical experimental setup for active, visual observation in a 3D world dubbed PESAO. The goal was to design PESAO for various active perception tasks with human subjects (active observers) capable of tracking the head and gaze.
Boundedness Condition for Anisotropic Norm of Linear Discrete Time-invariant Systems with Multiplicative Noise

Abstract In this paper, the anisotropy-based analysis problem for the linear discrete time-invariant system with state-multiplicative noises is considered. The mean anisotropy of stationary input sequence is assumed to be bounded by given nonnegative value. For this setting, the boundedness condition for anisotropic norm of the system is obtained in terms of Riccati equations.
Application of Assumption Modes and Effects Analysis to XMECA

Failure modes, effects and criticality analysis (FMECA) is a well-known risk assessment method used to diagnose potential failure modes of a product or system being designed. This method is much based on expert experience and aims to develop improvement strategies so as to reduce the risk of possible failures. Expert decisions and assumptions affect analysis results significantly, and when several experts are involved into analysis, obtaining final results becomes even more complicated and expert-dependent. XMECA is a generic extension of FMECA that can be applied to analysis not only failures, but other aspects related to safety and security analysis like intrusions (intrusion modes, effects and criticality analysis, IMECA) etc. This paper suggests a novel approach to support XMECA called assumption modes and effect analysis. This approach refers to a technique used to minimize risks involved in making assumptions done by expert during performance of XMECA. Possible assessment scenario are described, case study is provided.
You Had to Be There: Private Video Sharing for Mobile Phones using Fully Homomorphic Encryption

Smart phones and other small, low-cost video cameras have enabled the creation of billions of hours of video content. Many of these videos contain private content that the users want to share with a limited group of people, not all of whom they know. In this paper, we address the problem of sharing videos from different perspectives of the same event without either party revealing any content of the video until it has been established that the videos were of the same event. To address this problem, we designed and implemented a system that can detect similar videos and efficiently be run under a fully homomorphic encryption scheme on mobile devices. The resulting system was able to achieve a precision of 0.9932 and an F1 score of 0.9797 for determining the same videos. The algorithm was able to test a video in 0.4 seconds on an Android 9.0 Pixel 2 and run end-to-end in 1.5 seconds. As a result, participants in an event can share videos with each other to gain a full perspective of the event without releasing the videos to people who were not present.