Reviewerly/exHarmony huggingface dataset:
Tap and Swipe Activity Zones on Smartphones for Novice Older Adults

This article investigates tactile interaction on smartphones with adults aged 65 or older who were considered to have a novice level of skill with technology. Two experiments with two different groups of 40 Portuguese adults adds empirical evidence to the field and shows that older adult performance for tapping is best toward the center, right edge, and bottom right corner of the smartphone display. Results also show that a participant's performance of horizontal swipes is better with targets toward the bottom half of the display, while participant's performance of vertical swipes is better with targets toward the right half of the display. This article contributes to the body of research on the design of user interfaces for smartphones and mobile applications targeted at older adults, as well as providing practical information for designers and practitioners developing products that are more universally accessible.
Ghosting Effect Removal for Multi-Frame Super-Resolution on CCTV Videos with Moving Objects

With the increased use of closed-circuit television (CCTV) footage for security and surveillance purposes as well as for object or person recognition and efficiency monitoring, high-quality CCTV videos are necessary. In this paper, we propose Corgi Eye, a moving object removal + super-resolution framework for enhancing CCTV footages to remove ghosting artifacts caused by performing multi-frame super-resolution (MISR) on moving objects. Our method extends the framework of Eagle Eye, which is an existing MISR framework tailored for mobile devices. Our results demonstrate that the system can completely remove ghosting effects caused by moving objects while performing MISR on CCTV footage. Our proposed method demonstrates competitive performance when compared to Eagle Eye, achieving a 16% increase in terms of PSNR metric. Additionally, our method can produce clear images, on par with deep learning approaches such as ESPCN and SOF-VSR.
Technological scenarios for the new normality in Latin American academic libraries

The technological scenarios that have arisen in Latin American universities have been substantially changing in recent months as a result of the international health emergency caused by COVID-19. The lockdown policy revealed the need to develop digital and informational skills to adapt to the environment and the demands of distance learning required for integral online education. In order to observe and systematize the transformation of the technological environment in academic libraries during these months, the recent situation was identified based on data analysis obtained from a questionnaire applied in Latin American countries. The results reported significant changes in the use of digital applications, services, user assistance and librarian training during the pandemic, as well as the libraries’ mid-term commitments.
Solving Three-Dimensional Path Planning Problem Using a Visibility-Based Graphical Representation of the Design Space

Abstract Planning the shortest collision-free path among scattered obstacles is an NP-complete problem. As reviewed in this paper, a variety of deterministic as well as heuristic methods have been developed to address different instances of the problem. The focus of the deterministic methods is primarily on the optimality of the final solution and has been applied exclusively to regular shapes such as spheres or cubes. Nevertheless, due to the problem's intrinsic complexities (especially in 3D), researchers mainly resort to heuristics that offer acceptable (yet possibly suboptimal) solutions with reasonable resources. Therefore, less attention has been given to further the state-of-the-art in deterministic methods, which for 3D problems primarily focuses on approximating the solution. However, with the advancements in high-performance computing, we believe it is time to focus on solution quality. As such, this study aims to further the efforts in deterministic optimization methods for 3D path planning by overcoming some challenges of the existing methods and improving the optimality of the solution. The proposed approach is rooted in visibility-based planning methods where the obstacle-free space is modeled as a connectivity graph to be searched for the shortest path. The advantage of the proposed method in constructing the representative graph is that it does not make approximations to identify the graph nodes, unlike the existing methods. Nor does it limit the objects’ geometries to specific shapes such as blocks or spheres. The capability of the method in finding the shortest collision-free paths in environments cluttered with convex polyhedra is demonstrated using sample test problems.
Image inpainting based on directional Gaussian graph model using multi-head reference

This paper aims to repair missing regions which are corrupted along arbitrary directions. It presents a mixed image inpainting method based on Markov random field. By using Belief Propagation scheme in low gray-levels and alternately suggesting a directional Gaussian Graphical model (DGGM) for multiple references in a high-level range, it gains a balance between the model accuracy and the computation complexity in realization. On the basis of an existing method in [1], it improves the method in high level inpainting for the task under small train sets and large corrupted regions, by introducing these multi-head reference clues. Experimental results are given, the inpainting quality of different kinds of images with different sizes and contents under different parameter settings are compared in metrics of the peak signal noise ratio and the structural similarity index. The significance of parameter settings and the efficient computational cost could demonstrate the feasibility of this method.
Student Engagement in Online Teaching in South African Higher Education

This study assesses student engagement in an online teaching platform at South African universities. Online teaching and learning have expanded drastically in South Africa during the outbreak of COVID-19 despite few universities offering online teaching before the pandemic. The author found that most historically Black universities were affected mostly to offer online teaching and learning. This is because they lack access to social media platforms and information communication technology. While students from historical white universities enjoy access to data to conduct online teaching and learning, most students were discouraged to continue online teaching and learning because they felt isolated and fewer resources were allocated to online teaching activities. This chapter recommends the Department of Higher Education and Training in South Africa bridge the gap between rural and urban students by ensuring that all students are allocated data in order to embark on school assessments and homework.
Regularized Latent Space Exploration for Discriminative Face Super-Resolution

Learning face super-resolution models is challenged in many practical scenarios where high-resolution and low-resolution face pairs usually are difficult to collect for training examples. Recent self-supervised approach provides a feasible solution by using low-resolution faces to guide the generation of the corresponding high-resolution ones with a pretrained generator. In this paper, we propose a regularized latent space exploration approach to facilitate self-supervised face super-resolution. In the approach, a pretrained generative adversarial network (GAN) is fully used to control the exploration of high-resolution face generation in an iterative optimization manner for a low-resolution face. During the iteration, super-resolution faces are continually generated from a feasible latent space by the generator and evaluated by the discriminator, while the generator is online finetuned. The generation is evaluated by measuring the semantic loss as well as pixel loss between ground-truth low-resolution faces and the corresponding downsampled super-resolution faces. In this way, the generated faces can be appearance natural and semantic discriminative. Experiments validate the effectiveness of our approach in terms of quantitative metrics and visual quality.
A Remedy For Distributional Shifts Through Expected Domain Translation

Machine learning models often fail to generalize to unseen domains due to the distributional shifts. A family of such shifts, &#x201C;correlation shifts,&#x201D; is caused by spurious correlations in the data. It is studied under the overarching topic of &#x201C;domain generalization.&#x201D; In this work, we employ multi-modal translation networks to tackle the correlation shifts that appear when data is sampled out-of-distribution. Learning a generative model from training domains enables us to translate each training sample under the special characteristics of other possible domains. We show that by training a predictor solely on the generated samples, the spurious correlations in training domains average out, and the invariant features corresponding to true correlations emerge. Our proposed technique, Expected Domain Translation (EDT), is benchmarked on the Colored MNIST dataset and drastically improves the state-of-the-art classification accuracy by 38% with train-domain validation model selection.
Enhancing Effectiveness of E-Learning: Lessons from Technology Adoption Model

A number of policies, missions and programmes have been initiated to facilitate e-learning enabled by information and communication technologies (ICTs) over recent years both by central as well as state governments in order to enhance effectiveness of teaching-learning process. However, tangible outcomes of such initiatives have been limited so far due to a number of factors especially in government institutions. While in some cases the core reason has been availability, accessibility, adequacy and quality of ICT tools, in many cases intention to use such technologies by teachers has been found conspicuous by its absence. The present paper uses the theoretical framework of the Technology Acceptance Model (TAM) to deduce relevant lessons. The objective is not to deliberate on the nuances of the model but simply to pick out a few relevant constructs in light of the author’s own experience of technology adoption in the context of schools and colleges of Himachal Pradesh. After briefly outlining the key tenets of TAM, the paper articulates the rationale for adopting this model and finally discusses key lessons for increasing acceptance of such technologies among educational stakeholders. Since COVID 19 has already created a rationale for adopting technology-enabled learning methodologies, the recommendations of this paper are likely to improve the efficacy and effectiveness of current and future initiatives.
Simultaneous Integration of Multimodal Interfaces for Generating Structured and Reliable Robotic Task Configurations

This paper presents a framework that simultaneously integrates multiple input interfaces and extracts task parameters suitable for task execution in a human-robot collaborative environment. We used pointing gestures and natural language instruction as inputs as they provide the most natural interaction interfaces for humans. In the proposed method, the pointing gesture type and the pointing direction are estimated from RGB images, and the object being pointed at is inferred from the prior gesture information and the objects detected in the scene. Subsequently, the verbal command is parsed to extract task action, the object of interest along with its attributes and position in the 2D image frame. This extracted information from gesture recognition and verbal command is used to form task configurations for the desired human-robot collaborative tasks as well as to help resolve any uncertain or missing task parameters. The proposed framework shows very promising results in identifying the relevant task parameters for the intended robotic tasks in different real-world interaction scenarios.
Predicting the Generalization Gap in Deep Models using Anchoring

We address the problem of predicting the generalization gap of deep neural networks under large, natural, and synthetic distribution shifts between source and target domains. This is crucial in understanding how models behave in uncontrollable ‘in-the-wild’ scenarios, but existing techniques fail when target domain becomes very different from the source. Accurately capturing the relationship and distance between the source and target domains is critical for a reliable post-hoc estimation of generalization. In this paper, we propose a novel strategy for directly predicting accuracy on unseen target data with the help of anchoring and pre-text encoding in predictive models. Anchoring has been shown previously to perform effectively in characterizing domain shifts, which we exploit for predicting the generalization gap. Our experiments on the PACS dataset along with synthetic ablations indicate that our approach produces well calibrated accuracy estimates outperforming existing baselines.
Progress in Applying Valuated Binary Tree to Factorize Big Integers

Finding new methods to solve the hard problem of integer factorization has gone on for tens of years in cryptography but better methods are still in need. Even in the era of quantum computing, conventional approaches are necessary as a part of science researches. The method derived from valuated binary tree, which was raised in 2016, has revealed rigorous power in studying odd integers. This paper overviews the method from its origination to its achievements so as to make people know the method well. Four characteristics of symmetric property, genetic property, boundary property and connection property are introduced with their main traits. Achievements are exhibited briefly in integer factorization of both general purpose and special purpose. The paper shows that, the new method might be rigorous and prosperous in the future.
Adaptive Weighted Network With Edge Enhancement Module For Monocular Self-Supervised Depth Estimation

Monocular self-supervised depth estimation can be easily applied in many areas since only a single camera is required. However, current methods do not predict well in depth borders. Besides, factors such as occlusion and texture sparsity can lead to the failure of the photometric consistency, affecting the prediction performance. To overcome these deficiencies, an adaptive weighted monocular self-supervised depth estimation framework that exploits enhanced edge information and texture sparsity based adaptive weights is proposed. In particular, a module named edge enhancement module (EEM) is designed to be embedded into the current depth prediction network to extract edge details for clearer depth prediction in depth borders. Moreover, a texture sparsity based adaptive weighted (TSAW) loss is introduced to as-sign different weights according to texture sparsity, enabling a more targeted construction of geometric constraints. Experimental results on the KITTI dataset demonstrate that the proposed network outperforms state-of-the-art methods.
Scientific Applications of Machine Learning Algorithms

This chapter considers several popular scientific applications of machine learning/data mining algorithms. The survey-like introductory section provides a brief overview of some relevant historical and trending applications, while the other four sections present specific details on four selected scientific applications. Each section focuses on one of the following algorithms: neural networks, rule induction, tree algorithms, and neighborhood-based algorithms.
Cloud Computing Applications in Social Work and Education

This chapter provides overall information related to cloud computing technology. Initially, it explains an overview history of the technology. In addition, the components of cloud computing and how these components interact each other are described for a better understanding of lay persons. The distinctive fundamental characteristics of cloud computing are also listed. The chapter illuminates the service and deployment models of cloud computing. Moreover, the benefits and challenges are examined. Another section of the chapter highlights possible implementations of cloud computing in education and compares these implementations between the developing and developed country perspectives. More specifically, the services currently being used in the education domain are also emphasized. Finally, the possibility of using cloud computing in social work as an information technology or systems solutions is considered.
Transfer Learning based Precise Pose Estimation with Insufficient Data

With the recent advance in computer vision techniques and the growing utility of real-time human pose detection and tracking, deep learning-based pose estimation has been intensively studied in recent years. These studies rely on large-scale datasets of human pose images, for which expensive annotation jobs are required due to the complex spatial structure of pose keypoints. In this work, we present a transfer learning-based pose estimation model that leverages low-cost synthetic datasets and regressive domain adaptation, enabling the sample-efficient learning on precise human poses. In evaluation, we demonstrate that our model achieves the high accurate pose estimation on a dataset of golf swing images, which is targeted for a virtual golf coaching application.
Tangible and intangible spaces of community connectivity

This forum is dedicated to exploring the notion of meaningfulness in design processes, taking the perspectives of community groups, nongovernmental organizations, and those who are marginalized in society as starting points. Authors will reflect conceptually and methodologically on practical engagements. --- Rosanna Bellini and Angelika Strohmayer, Editors
Conditionally Factorized Variational Bayes with Importance Sampling

Coordinate ascent variational inference (CAVI) is a popular approximate inference method; however, it relies on a mean-field assumption that can lead to large estimation errors for highly correlated variables. In this paper, we propose a conditionally factorized variational family with an adjustable conditional structure and derive the corresponding coordinate ascent algorithm for optimization. The algorithm is termed Conditionally factorized Variational Bayes (CVB) and implemented with importance sampling. We show that by choosing a finer conditional structure, our algorithm can be guaranteed to achieve a better variational lower bound, thus providing a flexible trade-off between computational cost and inference accuracy. The validity of the method is demonstrated in a simple posterior computation task.
Outcomes of Cyber-Victimization and Bystander Reactions in Online Brand Communities

Online brand communities such as Facebook fan pages show an increasing level of cyber-victimization, which is where a community member is bullied by another member. Based on the cyber-victimization literature in psychology, we designed an experiment that explores how cyber-victimization affects three commercial outcomes, namely, a victim’s positive word-of-mouth intentions, community satisfaction, and community following intentions. Using a survey of 387 community members with past cyber-victimization experiences, our results show that outcomes differ significantly in relation to the severity of cyber-victimization (severe vs. mild) and the reactions (defending vs. reinforcing vs. pretending) from bystanders (i.e., uninvolved community members who witness the cyber-victimization incident). Our findings offer brand managers a better understanding of the undesirable effects of cyber-victimization in online brand communities, and of the type of reactions from bystanders that they may wish to encourage.
A CPM signal denoising method based on attention network

Cognitive communication countermeasure system utilizes artificial intelligence technology to quickly realize electromagnetic dynamic perception and electronic jamming strategy generation. In the complex electromagnetic environment of the modern battlefield, continuous phase modulation (CPM) signals are getting more and more attention due to high spectral efficiency and power efficiency. CPM signal denoising processing helps to improve electromagnetic dynamic perception performance. In this paper, a novel model, namely attentional denoising autoencoder (ADE), is proposed with enhanced signal denoising by introducing self-attentional mechanism into the autoencoder. The proposed method divides the one-dimensional communication signal sequence into fixed-size signal patches satisfying the same modulation law, and then utilizes the parallel computing of the self-attention mechanism to model the dependencies between the signal patches, and finally average pooling is used to synthesize the information of each signal patch to reconstruct the signal. The simulation results demonstrate that the proposed model is superior to other methods in terms of the denoising effect, and has a high degree of waveform recovery, which is helpful for the subsequent perception and processing of CPM signals.
Monitoring Social Distancing With Real-Time Detection and Tracking

Social distancing is one of the suggested solutions by the health authorities to reduce the spreading speed of the COVID-19 in public areas. A six-foot physical distancing has been set by the majority of public governors as a mandatory social regulation. However, it is difficult to monitor whether individuals practice the social distancing regulation or not. State-of-the-art technologies, such as computer visions, artificial intelligence, and big data analytics, can help for automated people detection and tracking in the crowd for indoor and outdoor environments using surveillance cameras. In this chapter, several types of popular object detection and tracking schemes in monitoring social distancing are illustrated with implementations of a cutting-edge human detection model by testing its reliability using a sample video. A real-world case study for social control management system is also introduced with its architecture designs and implementations in the context of the COVID-19 pandemic.
Remote Monitoring and Recognition of Physical Activities of Elderly People Using Smartphone Accelerometer Sensor Data Using Deep Learning Models

Remote monitoring and recognition of physical activities of elderly people within smart homes and detection of the deviations in their daily activities from previous behavior is one of the fundamental research challenges for the development of ambient assisted living system. This system is also very helpful in monitoring the health of a swiftly aging population in the developed countries. In this chapter, a framework is proposed for remote monitoring and recognition of physical activities of elderly people using smart phone accelerometer sensor data using deep learning models. The main objective of the proposed framework is to provide preventive measures for the emergency health issues such as cardiac arrest, sudden falls, dementia, or arthritis. For the performance evaluation of the proposed framework, two different benchmark accelerometer sensor datasets, UCI and WISDM, are used. Results analysis confirms the performance of the proposed scheme in terms of accuracy, F1-score, root-mean square error (RMSE).