Reviewerly/exHarmony huggingface dataset:
Smart Nursing Homes: Self-Management Architecture Based on IoT and Machine Learning for Rural Areas

The rate of world population aging is increasing. This situation directly affects all countries socially and economically, increasing their compromise and effort to improve the living conditions of this sector of society. In environments with large influxes of elderly people, such as nursing homes, the use of technology has shown promise in improving their quality of life. The use of smart devices allows people to automate everyday tasks and learn from them to predict future actions. Additionally, smartphones capture a wealth of information that allows to adapt to nearby actuators according to people’s preferences and even detects anomalies in their behaviour. Current works are proposing new frameworks to detect these behaviours and act accordingly. However, these works are not focused on managing multidevice environments where sensor and smartphone data are considered to automate environments with elderly people or to learn from them. Also, most of these works require a permanent Internet connection, so the full benefit of smart devices is not completely achieved. In this work, we present an architecture that takes the data from sensors and smartphones in order to adapt the behaviour of the actuators of the environment. In addition, it uses this data to learn from the environment to predict actions or to extrapolate the actions that should be executed according to similar behaviours. The architecture is implemented through a use case based on a nursing home located in a rural area. Thanks to this work, the quality of life of the elderly is improved in a simple, affordable, and transparent way for them.
Semantically Derived Geometric Constraints for MVS Reconstruction of Textureless Areas

Conventional multi-view stereo (MVS) approaches based on photo-consistency measures are generally robust, yet often fail in calculating valid depth pixel estimates in low textured areas of the scene. In this study, a novel approach is proposed to tackle this challenge by leveraging semantic priors into a PatchMatch-based MVS in order to increase confidence and support depth and normal map estimation. Semantic class labels on image pixels are used to impose class-specific geometric constraints during multiview stereo, optimising the depth estimation on weakly supported, textureless areas, commonly present in urban scenarios of building facades, indoor scenes, or aerial datasets. Detecting dominant shapes, e.g., planes, with RANSAC, an adjusted cost function is introduced that combines and weighs both photometric and semantic scores propagating, thus, more accurate depth estimates. Being adaptive, it fills in apparent information gaps and smoothing local roughness in problematic regions while at the same time preserves important details. Experiments on benchmark and custom datasets demonstrate the effectiveness of the presented approach.
Development of the cybersecurity scale (CS-S): Evidence of validity and reliability

This study aimed to develop a cybersecurity scale to measure individuals’ practices and perceptions regarding cybersecurity. The study tested psychometric properties of the Cybersecurity Scale (CS-S) by employing a multi-stage research design. In the first study, an Exploratory-Factor-Analysis (EFA) was conducted to explore the underlying factor structure and evaluate internal consistency reliability of the CS-S. The EFA results showed good internal consistency reliability (α = .88) and a six-factor structure. In the second study, a Confirmatory-Factor-Analysis (CFA) was conducted to verify the factor structure. The CFA results indicated that the six-factor model (i.e., confidentiality, control/possession, integrity, authenticity, availability, and utility) fits the data well. Significant individual differences were observed in each dimension of the CS-S. Results indicated that the CS-S has evidence of convergent, discriminant, and construct validity along with internal consistency reliability. The results suggested that the CS-S is a valid and reliable instrument to measure individuals’ cybersecurity practices and perceptions.
Efficient Revocable Multi-Authority Attribute-Based Encryption for Cloud Storage

As is known, attribute-based encryption (ABE) is usually adopted for cloud storage, both for its achievement of fine-grained access control over data, and for its guarantee of data confidentiality. Nevertheless, single-authority attribute-based encryption (SA-ABE) has its obvious drawback in that only one attribute authority can assign the users' attributes, enabling the data to be shared only within the management domain of the attribute authority, while rendering multiple attribute authorities unable to share the data. On the other hand, multi-authority attribute-based encryption (MA-ABE) has its advantages over SA-ABE. It can not only satisfy the need for the fine-grained access control and confidentiality of data, but also make the data shared among different multiple attribute authorities. However, existing MA-ABE schemes are unsuitable for the devices with resources-constraint, because these schemes are all based on expensive bilinear pairing. Moreover, the major challenge of MA-ABE scheme is attribute revocation. So far, many solutions in this respect are not efficient enough. In this paper, on the basis of the elliptic curves cryptography, we propose an efficient revocable multi-authority attribute-based encryption (RMA-ABE) scheme for cloud storage. The security analysis indicates that the proposed scheme satisfies indistinguishable under adaptive chosen plaintext attack assuming hardness of the decisional Diffie-Hellman problem. Compared with the other schemes, the proposed scheme gets its advantages in that it is more economical in computation and storage.
Text Entry in Virtual Environments using Speech and a Midair Keyboard

Entering text in virtual environments can be challenging, especially without auxiliary input devices. We investigate text input in virtual reality using hand-tracking and speech. Our system visualizes users' hands in the virtual environment, allowing typing on an auto-correcting midair keyboard. It also supports speaking a sentence and then correcting errors by selecting alternative words proposed by a speech recognizer. We conducted a user study in which participants wrote sentences with and without speech. Using only the keyboard, users wrote at 11 words-per-minute at a 1.2% error rate. Speaking and correcting sentences was faster and more accurate at 28 words-per-minute and a 0.5% error rate. Participants achieved this performance despite half of sentences containing an uncommon out-of-vocabulary word (e.g. proper name). For sentences with only in-vocabulary words, performance using speech and midair keyboard corrections was faster at 36 words-per-minute with a low 0.3% error rate.
Dynamic Imaging Using a Deep Generative SToRM (Gen-SToRM) Model

We introduce a generative smoothness regularization on manifolds (SToRM) model for the recovery of dynamic image data from highly undersampled measurements. The model assumes that the images in the dataset are non-linear mappings of low-dimensional latent vectors. We use the deep convolutional neural network (CNN) to represent the non-linear transformation. The parameters of the generator as well as the low-dimensional latent vectors are jointly estimated only from the undersampled measurements. This approach is different from traditional CNN approaches that require extensive fully sampled training data. We penalize the norm of the gradients of the non-linear mapping to constrain the manifold to be smooth, while temporal gradients of the latent vectors are penalized to obtain a smoothly varying time-series. The proposed scheme brings in the spatial regularization provided by the convolutional network. The main benefit of the proposed scheme is the improvement in image quality and the orders-of-magnitude reduction in memory demand compared to traditional manifold models. To minimize the computational complexity of the algorithm, we introduce an efficient progressive training-in-time approach and an approximate cost function. These approaches speed up the image reconstructions and offers better reconstruction performance.
The climate of climate change: Impoliteness as a hallmark of homophily in YouTube comment threads on Greta Thunberg's environmental activism

This paper investigates impoliteness and value homophily (‘thinking alike’) in the context of YouTube-based ideological discussions beneath the videos critical towards the Swedish environmental activist – Greta Thunberg. Drawing on the idea of rapport management, the study finds a remarkable scale of homophily as the postings follow recurrent patterns of face and sociality rights attacks echoing the same point of view. Consequently, while impoliteness has been recognized as widespread in social media for reasons such as anonymity and social detachment, this paper offers an insight into how the phenomenon contributes to the process of consolidation and homogenization of views through social comparison. As the study concludes, impoliteness in ideological discussions on YouTube may serve as the glue to ad hoc social contact between like-minded individuals –ultimately leading to social identification in relevant groups and formation of homophilous online communities.
Features Guided Face Super-Resolution via Hybrid Model of Deep Learning and Random Forests

Face hallucination or super-resolution is a practical application of general image super-resolution which has been recently studied by many researchers. The challenge of good face hallucination comes from a variety of poses, illuminations, facial expressions, and other degradations. In many proposed methods, researchers resolve it by using a generative neural network to reduce the perceptual loss so we can generate a photo-realistic image. The problem is that researchers usually overlook the fidelity of the super-resolved image which could affect further facial image processing. Meanwhile, many CNN based approaches cascade multiple networks to extract facial prior information to improve super-resolution quality. Because of the end-to-end design, the details are missing for investigation. In this paper, we combine new techniques in convolutional neural network and random forests to a Hierarchical CNN based Random Forests (HCRF) approach for face super-resolution in a coarse-to-fine manner. In the proposed approach, we focus on a general approach that can handle facial images with various conditions without pre-processing. To the best of our knowledge, this is the first paper that combines the advantages of deep learning with random forests for face super-resolution. To achieve superior performance, we propose two novel CNN models for coarse facial image super-resolution and segmentation and then apply new random forests to target on local facial features refinement making use of the segmentation results. Extensive benchmark experiments on subjective and objective evaluation show that HCRF can achieve comparable speed and competitive performance compared with state-of-the-art super-resolution approaches for very low-resolution images.
DronePrint

With the ubiquitous availability of drones, they are adopted benignly in multiple applications such as cinematography, surveying, and legal goods delivery. Nonetheless, they are also being used for reconnaissance, invading personal or secure spaces, harming targeted individuals, smuggling drugs and contraband, or creating public disturbances. These malicious or improper use of drones can pose significant privacy and security threats in both civilian and military settings. Therefore, it is vital to identify drones in different environments to assist the decisions on whether or not to contain unknown drones. While there are several methods proposed for detecting the presence of a drone, they have limitations when it comes to low visibility, limited access, or hostile environments. In this paper, we propose DronePrint that uses drone acoustic signatures to detect the presence of a drone and identify the make and the model of the drone. We address the shortage of drone acoustic data by relying on audio components of online videos. In drone detection, we achieved 96% accuracy in a closed-set scenario, and 86% accuracy in a more challenging open-set scenario. Our proposed method of cascaded drone identification, where a drone is identified for its 'make' followed by the 'model' of the drone achieves 90% overall accuracy. In this work, we cover 13 commonly used commercial and consumer drone models, which is to the best of understanding is the most comprehensive such study to date. Finally, we demonstrate the robustness of DronePrint to drone hardware modifications, Doppler effect, varying SNR conditions, and in realistic open-set acoustic scenes.
A deep residual computation model for heterogeneous data learning in smart Internet of Things

Smart Internet of Things (smart IoT) have emerged as a transformative computing paradigm recently. This new approach has made great contributions in the area of cyber–physical–social systems by employing various computational intelligence techniques like deep learning, for analyzing data, especially heterogeneous data from sensing and wireless communication. As a representative example of deep learning, deep residual networks have achieved excellent performance for big data feature learning since they can avoid gradient vanishing issues in deep learning models effectively. Unfortunately, they could not learn features for heterogeneous data, especially multi-modal data, in smart IoT. This paper proposes a deep residual computation model by generalizing the deep residual network in the tensor space. Especially, each multi-modal data object is represented as a tensor, while all hidden layers are also represented as tensors. Furthermore, we propose a tensor back-propagation algorithm to train the parameters of the deep residual computation model. Finally, we conduct extensive experiments to evaluate the presented deep residual model by comparing with the existing models such as multi-modal deep learning models, 3D deep residual models, deep computation models, and deep convolutional computation models. Results show that the proposed model produces more accurate classification results than other models for heterogeneous data feature learning in cyber–physical–social systems.
Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology

Deformable registration of magnetic resonance images between patients with brain tumors and healthy subjects has been an important tool to specify tumor geometry through location alignment and facilitate pathological analysis. Since tumor region does not match with any ordinary brain tissue, it has been difficult to deformably register a patient's brain to a normal one. Many patient images are associated with irregularly distributed lesions, resulting in further distortion of normal tissue structures and complicating registration's similarity measure. In this work, we follow a multi-step context-aware image inpainting framework to generate synthetic tissue intensities in the tumor region. The coarse image-to-image translation is applied to make a rough inference of the missing parts. Then, a feature-level patch-match refinement module is applied to refine the details by modeling the semantic relevance between patch-wise features. A symmetry constraint reflecting a large degree of anatomical symmetry in the brain is further proposed to achieve better structure understanding. Deformable registration is applied between inpainted patient images and normal brains, and the resulting deformation field is eventually used to deform original patient data for the final alignment. The method was applied to the Multimodal Brain Tumor Segmentation (BraTS) 2018 challenge database and compared against three existing inpainting methods. The proposed method yielded results with increased peak signal-to-noise ratio, structural similarity index, inception score, and reduced L1 error, leading to successful patient-to-normal brain image registration.
The e-Learning of Students and University’s Brand Image (Post COVID-19): How Successfully Al-Ain University Have Embraced the Paradigm Shift in Digital Learning

The COVID-19 has caused a state of emergency not only in the UAE but also around the globe. Following the preventive measures, the government of UAE has closed temporarily all types of educational institutes, replacing them by other institutes, thus shifting to e-learning. Therefore, this study aims at examining the role of information and communication technology, e-service quality, and e-information quality towards the brand image of universities by concentrating on students' e-learning. More specifically, this study investigates the impact of the information and communication ethnobiology post COVID-19 on the university's brand image of Alain University in UAE as one of the leading universities. The contribution of the study is twofold. First, the study is among the pioneering studies that have proposed a framework to develop an excellent online learning system, which can promote the university's brand image. Second, this study fills the gap by investigating the subject in the context of a Gulf country, which is among the most affected thirty countries due to COVID-19. It is, therefore, a critical contemporary issue for governments, higher education commissions, and universities to understand the antecedents of the online learning system and their relationships with the university's brand image.
Path Following Control for UAV Using Deep Reinforcement Learning Approach

Unmanned aerial vehicles (UAVs) have been extensively used in civil and industrial applications due to the rapid development of the guidance, navigation and control (GNC) technologies. Especially, using deep reinforcement learning methods for motion control acquires a major progress recently, since deep [Formula: see text]-learning algorithm has been successfully applied to the continuous action domain problem. This paper proposes an improved deep deterministic policy gradient (DDPG) algorithm for path following control problem of UAV. A specific reward function is designed for minimizing the cross-track error of the path following problem. In the training phase, a double experience replay buffer (DERB) is used to increase the learning efficiency and accelerate the convergence speed. First, the model of UAV path following problem has been established. After that, the framework of DDPG algorithm is constructed. Then the state space, action space and reward function of the UAV path following algorithm are designed. DERB is proposed to accelerate the training phase. Finally, simulation results are carried out to show the effectiveness of the proposed DERB–DDPG method.
Law, mobile money drivers and mobile money innovations in developing countries

This study investigates how the rule of law (i.e. law) modulates demand- and supply-side drivers of mobile money to influence mobile money innovations (i.e. mobile money accounts, the mobile phone used to send money and the mobile phone used to receive money) in developing countries. The following findings from Tobit regressions are established. First, from the demand-side linkages, law modulates: (i) bank accounts and automated teller machine (ATM) penetration for negative interactive relationships with mobile money innovations and (ii) bank sector concentration for a positive interactive relationship with mobile money accounts. Second, from supply-side linkages, law interacts with: (i) mobile subscriptions for a negative relationship with the mobile phone used to send money; (ii) mobile connectivity coverage for a negative nexus on the mobile phone used to receive money and (iii) mobile connectivity performance for a negative influence on the mobile phone used to send/receive money. Policy implications are discussed in the light of enhancing the rule of law as well as improving mobile phone subscription, connectivity and performance dynamics.
Sentiment Analysis Using Deep Learning

Emotion recognition from text is crucial Natural Language Processing task which can contribute enormous benefits to different areas such as artificial intelligence, human interaction with computers etc. Emotions are physiologic thoughts engendered in human reactions to the events. Analysis of these emotions without facial and voice modulation are critical and requires a supervisory approach for proper interpretation of emotions. In spite of these challenges, it's essential to acknowledge the human emotions as they progressively communicate using mistreatment text through social media applications such as Facebook, Twitter etc. In this paper, we propose a sentimental classification of multitude of tweets. Here, we use deep learning techniques to classify the sentiments of an expression into positive or negative emotions. The positive emotions are further classified into enthusiasm, fun, happiness, love, neutral, relief, surprise and negative emotions are classified into anger, boredom, emptiness, hate, sadness, worry. We experimented and evaluated the method using Recurrent Neural Networks and Long short-term memory on three different datasets to show how to achieve high emotion classification accuracy. A through evaluation shows that the system gains emotion prediction on LSTM model with 88.47% accuracy for positive/negative classification and 89.13% and 91.3% accuracy for positive and negative subclass respectively.
Discriminative Neural Clustering for Speaker Diarisation

In this paper, we propose Discriminative Neural Clustering (DNC) that formulates data clustering with a maximum number of clusters as a supervised sequence-to-sequence learning problem. Com-pared to traditional unsupervised clustering algorithms, DNC learns clustering patterns from training data without requiring an explicit definition of a similarity measure. An implementation of DNC based on the Transformer architecture is shown to be effective on a speaker diarisation task using the challenging AMI dataset. Since AMI contains only 147 complete meetings as individual input sequences, data scarcity is a significant issue for training a Transformer model for DNC. Accordingly, this paper proposes three data augmentation schemes: sub-sequence randomisation, input vector randomisation, and Diaconis augmentation, which generates new data samples by rotating the entire input sequence of L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -normalised speaker embeddings. Experimental results on AMI show that DNC achieves a reduction in speaker error rate (SER) of 29.4% relative to spectral clustering.
Leveraging Self-Supervision for Cross-Domain Crowd Counting

State-of-the-art methods for counting people in crowded scenes rely on deep networks to estimate crowd density. While effective, these data-driven approaches rely on large amount of data annotation to achieve good performance, which stops these models from being deployed in emergencies during which data annotation is either too costly or cannot be obtained fast enough. One popular solution is to use synthetic data for training. Unfortunately, due to domain shift, the resulting models generalize poorly on real imagery. We remedy this shortcoming by training with both synthetic images, along with their associated labels, and unlabeled real images. To this end, we force our network to learn perspective-aware features by training it to recognize upside-down real images from regular ones and incorporate into it the ability to predict its own uncertainty so that it can generate useful pseudo labels for fine-tuning purposes. This yields an algorithm that consistently outperforms state-of-the-art cross-domain crowd counting ones without any extra computation at inference time. Code is publicly available at https://github.com/weizheliu/Cross-Domain-Crowd-Counting.