Reviewerly/exHarmony huggingface dataset:
A Facial Expression Recognition Model using Lightweight Dense-Connectivity Neural Networks for Monitoring Online Learning Activities

State-of-the-art architectures of convolutional neural networks (CNN) are widely used by authors for facial expression recognition (FER).There are many variants of these models with positive results in studies for FER and successful applications, some well-known models are VGG, ResNet, Xception, EfficientNet, DenseNet.However, these models have considerable complexity for some real-world applications with limitations of computational resources.This paper proposes a lightweight CNN model based on a modern architecture of dense-connectivity with moderate complexity but still ensures quality and efficiency for facial expression recognition.Then, it is designed to be integrated into learning management systems (LMS) for recording and evaluation of online learning activities.The proposed model is to run experiments on some popular datasets for testing and evaluation, the results show that the model is effective and can be used in practice.
Performance Analysis of IoT Cloud-based Platforms using Quality of Service Metrics

There are several IoT platforms providing a variety of services for different applications.Finding the optimal fit between application and platform is challenging since it is hard to evaluate the effects of minor platform changes.Several websites offer reviews based on user ratings to guide potential users in their selection.Unfortunately, review data are subjective and sometimes conflictingindicating that they are not objective enough for a fair judgment.Scientific papers are known to be the reliable sources of authentic information based on evidence-based research.However, literature revealed that though a lot of work has been done on theoretical comparative analysis of IoT platforms based on their features, functions, architectures, security, communication protocols, analytics, scalability, etc., empirical studies based on measurable metrics such as response time, throughput, and technical efficiency, that objectively characterize user experience seem to be lacking.In an attempt to fill this gap, this study used web analytic tools to gather data on the performance of some selected IoT cloud platforms.Descriptive and inferential statistical models were used to analyze the gathered data to provide a technical ground for the performance evaluation of the selected IoT platforms.Results showed that the platforms performed differently in the key performance metrics (KPM) used.No platform emerged best in all the KPMs.Users' choice will therefore be based on metrics that are most relevant to their applications.It is believed that this work will provide companies and other users with quantitative evidence to corroborate social media data and thereby give a better insight into the performance of IoT platforms.It will also help vendors to improve on their quality of service (QoS).
BabyNet: Reconstructing 3D faces of babies from uncalibrated photographs

We present a 3D face reconstruction system that aims at recovering the 3D facial geometry of babies from uncalibrated photographs, BabyNet. Since the 3D facial geometry of babies differs substantially from that of adults, baby-specific facial reconstruction systems are needed. BabyNet consists of two stages: 1) a 3D graph convolutional autoencoder learns a latent space of the baby 3D facial shape; and 2) a 2D encoder that maps photographs to the 3D latent space based on representative features extracted using transfer learning. In this way, using the pre-trained 3D decoder, we can recover a 3D face from 2D images. We evaluate BabyNet and show that 1) methods based on adult datasets cannot model the 3D facial geometry of babies, which proves the need for a baby-specific method, and 2) BabyNet outperforms classical model-fitting methods even when a baby-specific 3D morphable model, such as BabyFM, is used.
<scp>QuoTe</scp> : Quality-oriented Testing for Deep Learning Systems

Recently, there has been significant growth of interest in applying software engineering techniques for the quality assurance of deep learning (DL) systems. One popular direction is DL testing—that is, given a property of test, defects of DL systems are found either by fuzzing or guided search with the help of certain testing metrics. However, recent studies have revealed that the neuron coverage metrics, which are commonly used by most existing DL testing approaches, are not necessarily correlated with model quality (e.g., robustness, the most studied model property), and are also not an effective measurement on the confidence of the model quality after testing. In this work, we address this gap by proposing a novel testing framework called QuoTe (i.e., Qu ality- o riented Te sting). A key part of QuoTe is a quantitative measurement on (1) the value of each test case in enhancing the model property of interest (often via retraining) and (2) the convergence quality of the model property improvement. QuoTe utilizes the proposed metric to automatically select or generate valuable test cases for improving model quality. The proposed metric is also a lightweight yet strong indicator of how well the improvement converged. Extensive experiments on both image and tabular datasets with a variety of model architectures confirm the effectiveness and efficiency of QuoTe in improving DL model quality—that is, robustness and fairness. As a generic quality-oriented testing framework, future adaptations can be made to other domains (e.g., text) as well as other model properties.
Gestural and Touchscreen Interaction for Human-Robot Collaboration: A Comparative Study

Close human-robot interaction (HRI), especially in industrial scenarios, has been vastly investigated for the advantages of combining human and robot skills. For an effective HRI, the validity of currently available human-machine communication media or tools should be questioned, and new communication modalities should be explored. This article proposes a modular architecture allowing human operators to interact with robots through different modalities. In particular, we implemented the architecture to handle gestural and touchscreen input, respectively, using a smartwatch and a tablet. Finally, we performed a comparative user experience study between these two modalities.
Developing Wearable Human–Computer Interfacing System Based on EMG and Gyro for Amputees

Electronics with low power consumption and the Internet of Things (IoT) are promoting the development of wearable medical devices. Persons who have amputated limbs by accident or by born and also want to operate practical devices and systems mostly personal computers then they have to be required a human–computer interface (HCI) system. As a result, innovations are essential for amputated persons in this area. To operate a computer, a keyboard and mouse are essential as control tools. Some may use the virtual keyboard to type or do multiple tasks, but that operation is quite slow or challenging for high-performance work mostly for amputees’ person. In this study, we present a wireless EMG data acquisition system that has a multichannel data transmission capability and an EMG and gyro-controlled combined mouse and keyboard that is acceptable for persons who have had their hands amputated up to the elbow. Here, we use left-hand and right-hand biceps muscles for mouse clicking and keyboard button pressing functions. Hand coordination is detected by a gyroscope sensor, and this hand coordination data are used to mouse cursor positioning and selecting keywords or other functionality of the keyboard. In this paper, we design a signal acquisition system with a wireless multichannel transmission circuit and a keyboard that is operated with EMG and gyroscope signals. The device described in this article is capable of performing all of the functions of a standard computer mouse and keyboard.
Gender Prediction of Indonesian Twitter Users Using Tweet and Profile Features

The increasing use of social media generates huge amounts of data which in turn triggers research into social media analytics. Social media contents can be analyzed to explore public opinion on an issue or provide the insights reflecting proxy indicators towards real-world events. Understanding the demographics of social media users can increase the potential for applications of sentiment analysis, topic modeling, and other analytical tasks. To map demographics, we need to know the latent attributes of users, such as age, gender, occupation and location of residence. Since this attribute is not directly available, we need to do some inference from the social media data. This study aims to predict the gender attribute given a Twitter user account. We conducted experiments with several supervised classifiers with feature extraction, including the use of word embedding representations. The results of this study indicate that the combination of features extracted from Tweet contents and user profile structured data can predict the gender of Twitter users in Indonesia with accuracy above 80%.
Did you Run the Telegram? Use of Mobile Spelling Checker on Academic Writing

Technology in education has a pivotal role in language learning in this era of globalization.This study aims to determine the impact of mobile spelling checker on academic writing among preuniversity students of a college in Malaysia.Two objectives are formed.The primary objective is to evaluate the result of the mobile spelling checker on academic writing produced by Malaysian pre-university students.Additionally, another objective is to investigate their perceptions regarding the mobile spelling checker utilised.The quantitative research approach was used.A survey questionnaire was employed to explore their perceptions and satisfaction about the spelling checker used.Cluster random sampling was employed.The samples consisted of 99 preuniversity students.The results provide light on the proper usage of mobile spelling checker for academic writing.The mobile spelling checker receives many favourable responses.The research concludes that the mobile spelling checker can meet the diverse demands of pre-university students in addition to the linguistic requirements of core courses and industry.
Annular Computational Imaging: Capture Clear Panoramic Images Through Simple Lens

Panoramic Annular Lens (PAL) composed of few lenses has great potential in panoramic surrounding sensing tasks for mobile and wearable devices because of its tiny size and large Field of View (FoV). However, the image quality of tiny-volume PAL confines to optical limit due to the lack of lenses for aberration correction. In this paper, we propose an Annular Computational Imaging (ACI) framework to break the optical limit of light-weight PAL design. To facilitate learning-based image restoration, we introduce a wave-based simulation pipeline for panoramic imaging and tackle the synthetic-to-real gap through multiple data distributions. The proposed pipeline can be easily adapted to any PAL with design parameters and is suitable for loose-tolerance designs. Furthermore, we design the Physics Informed Image Restoration Network (PI <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> RNet) considering the physical priors of panoramic imaging and single-pass physics-informed engine. At the dataset level, we create the DIVPano dataset and the extensive experiments on it illustrate that our proposed network sets the new state of the art in the panoramic image restoration under spatially-variant degradation. In addition, the evaluation of the proposed ACI on a simple PAL with only 3 spherical lenses reveals the delicate balance between high-quality panoramic imaging and compact design. To the best of our knowledge, we are the first to explore Computational Imaging (CI) in PAL. <xref ref-type="fn" rid="fn1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><sup/></xref>
Estimating social distance in public places for COVID-19 protocol using region CNN

The Coronavirus disease has spread throughout the world and its fear has made people to be more cautious in public places. Since precautionary measures are the only reliable protocol to defend ourselves, social distancing is the only best approach to defend against the pandemic situation. The reproduction number i.e. R0 factor of COVID-19, can be slowed down only through the physical distancing norms. This research proposes a deep learning approach for maintaining the social distance by tracking and detecting the people present indoor and outdoor scenarios. Surveillance video is taken as the input and applied into you only look once (YOLO) V3 algorithm. The persons in the video are identified based on the segmentation algorithm present within the framework and then using Euclidean distance the image is evaluated. The bounding box algorithm helps to segregate the humans based on the minimum distance threshold. The proposed method is evaluated for images with peoples in the market, availing essential commodities and students entry inside a campus. Our proposed region-based convolutional neural network (RCNN) algorithm gives a better accuracy over the traditional models and hence the service can be implemented in general for places where social distancing is mandatory.
Exploring the scaling limitations of the variational quantum eigensolver with the bond dissociation of hydride diatomic molecules

Abstract Materials simulations involving strongly correlated electrons pose fundamental challenges to state‐of‐the‐art electronic structure methods but are hypothesized to be the ideal use case for quantum computing algorithms. To date, no quantum computer has simulated a molecule of a size and complexity relevant to real‐world applications, despite the fact that the variational quantum eigensolver (VQE) algorithm can predict chemically accurate total energies. Nevertheless, because of the many applications of moderately sized, strongly correlated systems, such as molecular catalysts, the successful use of the VQE stands as an important waypoint in the advancement toward useful chemical modeling on near‐term quantum processors. In this paper, we take a significant step in this direction. We lay out the steps, write, and run parallel code for an (emulated) quantum computer to compute the bond dissociation curves of the TiH, LiH, NaH, and KH diatomic hydride molecules using the VQE. TiH was chosen as a relatively simple chemical system that incorporates d orbitals and strong electron correlation. Because current VQE implementations on existing quantum hardware are limited by qubit error rates, the number of qubits available, and the allowable gate depth, recent studies using it have focused on chemical systems involving s and p block elements. Through VQE + UCCSD calculations of TiH, we evaluate the near‐term feasibility of modeling a molecule with d‐orbitals on real quantum hardware. We demonstrate that the inclusion of d‐orbitals and the use of the UCCSD ansatz, which are both necessary to capture the correct TiH physics, dramatically increase the cost of this problem. We estimate the approximate error rates necessary to model TiH on current quantum computing hardware using VQE + UCCSD and show them to likely be prohibitive until significant improvements in hardware and error correction algorithms are available.
Design principles of interactive play systems for children’s outdoor play: A designers’ perspective

In recent years, designers around the world have developed a number of interactive play systems to stimulate children's outdoor play. This paper provides a framework for the design principles used by designers of interactive play systems. In addition, the mechanisms by which these design principles could potentially contribute to stimulating children's outdoor play are discussed based on the COM-B model of behaviour change. The COM-B model is adopted to elaborate the design principles and their mechanisms of action. This model provides a basis for designing healthy behaviour change interventions. It defines behaviour as the result of an interaction between three components that generate behaviour: Capability, Opportunity, and Motivation. The contribution of this paper is to identify the design principles and link them to the underlying factors of children's outdoor play in terms of the components of the COM-B model. Clarifying this link elaborates the mechanisms of action of design principles to stimulate children's outdoor play. An in-depth interview approach is used to explore the designers' perspectives. The findings of this research contribute to the theory of behaviour change and are beneficial for practitioners, and in particular, interactive designers who develop systems that stimulate children's outdoor play.
Security Issues in Cloud Computing: A Study

Cloud computing is the study of using remote servers which are hosted on the internet to deliver on-demand computing resources on pay-for-use basis rather than a local server. It provides online services via third party which own the infrastructure. Due to the openness of cloud computing systems, customers have concerns about security point of view in the adoption of the services which they provide. The purpose of this paper is to give a detail regarding to the data security and privacy safety issues in cloud computing systems. A security architecture discussed in this paper to provide security measures. In this architecture it can be seen that every level provides security measures in different ways. Then this paper will also discuss some of the current available solutions. According to the findings of this research, in the designing phase of the applications which are based on cloud, there should be taken security measures to increase the trust level of customers’ and organizations.
Self-Distilled Hierarchical Network for Unsupervised Deformable Image Registration

Unsupervised deformable image registration benefits from progressive network structures such as Pyramid and Cascade. However, existing progressive networks only consider the single-scale deformation field in each level or stage and ignore the long-term connection across non-adjacent levels or stages. In this paper, we present a novel unsupervised learning approach named Self-Distilled Hierarchical Network (SDHNet). By decomposing the registration procedure into several iterations, SDHNet generates hierarchical deformation fields (HDFs) simultaneously in each iteration and connects different iterations utilizing the learned hidden state. Specifically, hierarchical features are extracted to generate HDFs through several parallel gated recurrent units, and HDFs are then fused adaptively conditioned on themselves as well as contextual features from the input image. Furthermore, different from common unsupervised methods that only apply similarity loss and regularization loss, SDHNet introduces a novel self-deformation distillation scheme. This scheme distills the final deformation field as the teacher guidance, which adds constraints for intermediate deformation fields on deformation-value and deformation-gradient spaces respectively. Experiments on five benchmark datasets, including brain MRI and liver CT, demonstrate the superior performance of SDHNet over state-of-the-art methods with a faster inference speed and a smaller GPU memory. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Blcony/SDHNet</uri> .
Simulating Wearable Urban Augmented Reality Experiences in VR: Lessons Learnt from Designing Two Future Urban Interfaces

Augmented reality (AR) has the potential to fundamentally change how people engage with increasingly interactive urban environments. However, many challenges exist in designing and evaluating these new urban AR experiences, such as technical constraints and safety concerns associated with outdoor AR. We contribute to this domain by assessing the use of virtual reality (VR) for simulating wearable urban AR experiences, allowing participants to interact with future AR interfaces in a realistic, safe and controlled setting. This paper describes two wearable urban AR applications (pedestrian navigation and autonomous mobility) simulated in VR. Based on a thematic analysis of interview data collected across the two studies, we find that the VR simulation successfully elicited feedback on the functional benefits of AR concepts and the potential impact of urban contextual factors, such as safety concerns, attentional capacity, and social considerations. At the same time, we highlight the limitations of this approach in terms of assessing the AR interface’s visual quality and providing exhaustive contextual information. The paper concludes with recommendations for simulating wearable urban AR experiences in VR.
POI Recommendation Model Using Multi-Head Attention in Location-Based Social Network Big Data

A point of interest (POI) recommendation model using deep learning in location-based social network big data is proposed. Firstly, the features of POI are divided into inherent features composed of attributes such as geographical location and category, and semantic features of relevance composed of spontaneous access by users. Secondly, the inherent attribute features and semantic features of POI are extracted by constraint matrix decomposition and word vector model respectively, and the two hidden vectors are spliced into the feature vectors of POI to solve the problems of data sparsity and cold start. Finally, the multi-head attention is used to obtain the key information of user preferences, and a deep learning recommendation framework is constructed to model the nonlinear interaction between features. Experiments show that when the recommendation list is 10, the precision and recall of the proposed method are 0.118 and 0.135 respectively, which are better than the comparative recommendation method.
Research on Improved Retinex-Based Image Enhancement Method for Mine Monitoring

An improved Retinex fusion image enhancement algorithm is proposed for the traditional image denoising methods and problems of halo enlargement and image overexposure after image enhancement caused by the existing Retinex algorithm. First, a homomorphic filtering algorithm is used to enhance each RGB component of the underground coal mine surveillance image and convert the image from RGB space to HSV space. Second, bilateral filtering and multi-scale retinex with color restoration (MSRCR) fusion algorithms are used to enhance the luminance V component while keeping the hue H component unchanged. Third, adaptive nonlinear stretching transform is used for the saturation S-component. Last, the three elements are combined and converted back to RGB space. MATLAB simulation experiments verify the superiority of the improved algorithm. Based on the same dataset and experimental environment, the improved algorithm has a more uniform histogram distribution than the multi-scale Retinex (msr) algorithm and MSRCR algorithm through comparative experiments. At the same time, the peak signal-to-noise ratio (PSNR), structural similarity (SSIM), standard deviation, average gradient, mean value, and colour picture information entropy of the images were improved by 8.28, 0.15, 4.39, 7.38, 52.92 and 2.04, respectively, compared to the MSR algorithm, and 3.97, 0.02, 34.33, 60.46, 26.21, and 1.33, respectively, compared to the MSRCR algorithm. The experimental results show that the image quality, brightness and contrast of the images enhanced by the improved Retinex algorithm are significantly enhanced, and the amount of information in the photos increases, the halo and overexposure in the images are considerably reduced, and the anti-distortion performance is also improved.