Reviewerly/exHarmony huggingface dataset:
Usability, inclusivity, and content evaluation of COVID-19 contact tracing apps in the United States

Abstract We evaluated the usability of mobile COVID-19 contact tracing apps, especially for individuals with barriers to communication and limited digital literacy skills. We searched the Apple App Store, Google Play, peer-reviewed literature, and lay press to find contact tracing apps in the United States. We evaluated apps with a framework focused on user characteristics and user interface. Of the final 26 apps, 77% were on both iPhone and Android. 69% exceeded 9th grade readability, and 65% were available only in English. Only 12% had inclusive illustrations (different genders, skin tones, physical abilities). 92% alerted users of an exposure, 42% linked to a testing site, and 62% linked to a public health website within 3 clicks. Most apps alert users of COVID-19 exposure but require high English reading levels and are not fully inclusive of the U.S. population, which may limit their reach as public health tools.
S2SAN: A sentence-to-sentence attention network for sentiment analysis of online reviews

Many existing attention-based deep learning approaches to sentiment analysis have focused on words and represent an entire review text as a word sequence. However, these approaches overlook the differences in the importance of each sentence to the complete text. To solve this problem, some work has been performed to calculate sentence-level attention, but these studies use the same approach that is applied to word-level attention, which leads to unnecessary sequential structures and increased complexity of sentence representation. Therefore, in this paper, we propose a sentence-to-sentence attention network1 (S2SAN) using multihead self-attention. We conducted several domain-specific, cross-domain and multidomain sentiment analysis experiments with real-world datasets. The experimental results show that S2SAN outperforms other state-of-the-art models. Some classical sentiment classifiers [e.g., convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM) models] achieve better accuracies when they are reconfigured to include sentence-to-sentence attention.
Contextualized Graph Attention Network for Recommendation with Item Knowledge Graph

Graph neural networks (GNN) have recently been applied to exploit knowledge graph (KG) for recommendation. Existing GNN-based methods explicitly model the dependency between an entity and its local graph context in KG (i.e., the set of its first-order neighbors), but may not be effective in capturing its non-local graph context (i.e., the set of most related high-order neighbors). In this paper, we propose a novel recommendation framework, named Contextualized Graph Attention Network (CGAT), which can explicitly exploit both local and non-local graph context information of an entity in KG. More specifically, CGAT captures the local context information by a user-specific graph attention mechanism, considering a user's personalized preferences on entities. In addition, CGAT employs a biased random walk sampling process to extract the non-local context of an entity, and utilizes a Recurrent Neural Network (RNN) to model the dependency between the entity and its non-local contextual entities. To capture the user's personalized preferences on items, an item-specific attention mechanism is also developed to model the dependency between a target item and the contextual items extracted from the user's historical behaviors. We compared CGAT with state-of-the-art KG-based recommendation methods on real datasets, and the experimental results demonstrate the effectiveness of CGAT.
Enhancing Virtual Walking Sensation Using Self-Avatar in First-Person Perspective and Foot Vibrations

Walking is a fundamental physical activity in humans. Various virtual walking systems have been developed using treadmill or leg-support devices. Using optic flow, foot vibrations simulating footsteps, and a walking avatar, we propose a virtual walking system that does not require limb action for seated users. We aim to investigate whether a full-body or hands-and-feet-only walking avatar with either the first-person (experiment 1) or third-person (experiment 2) perspective can convey the sensation of walking in a virtual environment through optic flows and foot vibrations. The viewing direction of the virtual camera and the head of the full-body avatar were linked to the actual user's head motion. We discovered that the full-body avatar with the first-person perspective enhanced the sensations of walking, leg action, and telepresence, either through synchronous or asynchronous foot vibrations. Although the hands-and-feet-only avatar with the first-person perspective enhanced the walking sensation and telepresence, compared with the no-avatar condition, its effect was less prominent than that of the full-body avatar. However, the full-body avatar with the third-person perspective did not enhance the sensations of walking and leg action; rather, it impaired the sensations of self-motion and telepresence. Synchronous or rhythmic foot vibrations enhanced the sensations of self-motion, waking, leg action, and telepresence, irrespective of the avatar condition. These results suggest that the full-body or hands-and-feet avatar is effective for creating virtual walking experiences from the first-person perspective, but not the third-person perspective, and that the foot vibrations simulating footsteps are effective, regardless of the avatar condition.
EFNet: Enhancement-Fusion Network for Semantic Segmentation

Semantic segmentation is a challenging and important task in computer vision. Convolutional neural networks (CNNs) have demonstrated their outstanding performances on such dense classification tasks. Most recent segmentation networks mainly focus on feature extraction for one single input image, while paying little attention to facilitating the segmentation by image manipulation or enhancement. In this paper, we design an enhancement-fusion network (EFNet), which aims at enhancing an input image for more diversified features to boost the following task of pixel-wise labeling. Specifically, the enhancement modules are trained to produce multiple enhanced images. Afterwards, the fusion module selectively attends on such images and fuses them to yield one new image. The proposed EFNet can be directly and flexibly integrated as an auxiliary network with state-of-the-art semantic segmentation networks, while maintaining the end-to-end training manner. Extensive results on benchmark datasets corroborate that the combination of the EFNet and the CNN-based semantic segmentation networks significantly improves the segmentation performance compared with the original segmentation networks.
Towards Fast and Robust Real Image Denoising With Attentive Neural Network and PID Controller

With the development of deep learning technologies, recent research on real-world noisy image denoising has achieved a considerable improvement in performance. However, a common limitation for existing approaches is the imbalanced trade-off between denoising accuracy and efficiency. To address this problem, we propose a robust and efficient denoiser, called a hierarchical-based PID-attention denoising network (HPDNet), to flexibly deal with the sophisticated noise. The core of our algorithm is the PID-attentive recurrent network (PAR-Net) whose framework mainly consists of the LSTM network and PID controller. PAR-Net inherits the advantages of both the attentive recurrent network and control action, which can encourage more discriminatory feature representations. This learning procedure is implemented within a feedback control system, allowing a faster and more robust means to enhance feature discriminability. Furthermore, by decomposing the noisy image and stacking the PAR-Nets, our PAR-Net can work on a progressively hierarchical framework, and hence obtain multi-scale features and manageable successive refinements. On several widely used datasets, the proposed HPDNet demonstrates high efficiency, while delivering a better perceptually appealing image quality over state-of-the-art image denoising methods.
Collision-free Path Planning for UAVs using Efficient Artificial Potential Field Algorithm

Unmanned Aerial Vehicles (UAVs), a new emerging form of Internet of Things (IoT), is a promising technology to be widely used in both civil and military applications. On the fly, the UAVs need to find an efficient and safe path by avoiding both static and dynamic obstacles to carry out any mission successfully. The Artificial Potential Field (APF) algorithm is one of the conventional catalysts in UAV path planning. However, APF-aided UAVs can be easily trapped into a local minimum solution before reaching the destination. Therefore, this paper proposes an efficient APF algorithm for Collision-free Path Planning (eAPF-CPP) in UAVs. In eAPF-CPP, the attractive and repulsive potentials evaluate the quadratic distance to the destination and the obstacle respectively. The evaluation aids the UAV to select the optimal path in navigation. The eAPF-CPP mechanism is simulated in the Software-In-The-Loop (SITL) setup, and the experimental results show that the eAPF-CPP mechanism utilizes an average of 24.4 seconds to track a safe path and has a lower collision rate of 8.56% compared with Artifical Potential Field Approach (APFA).
Graph2Net: Perceptually-Enriched Graph Learning for Skeleton-Based Action Recognition

Skeleton representation has attracted a great deal of attention recently as an extremely robust feature for human action recognition. However, its non-Euclidean structural characteristics raise new challenges for conventional solutions. Recent studies have shown that there is a native superiority in modeling spatiotemporal skeleton information with a Graph Convolutional Network (GCN). Nevertheless, the skeleton graph modeling normally focuses on the physical adjacency of the elements of the human skeleton sequence, which contrasts with the requirement to provide a perceptually meaningful representation. To address this problem, in this paper, we propose a perceptually-enriched graph learning method by introducing innovative features to spatial and temporal skeleton graph modeling. For the spatial information modeling, we incorporate a Local-Global Graph Convolutional Network (LG-GCN) that builds a multifaceted spatial perceptual representation. This helps to overcome the limitations caused by over-reliance on the spatial adjacency relationships in the skeleton. For temporal modeling, we present a Region-Aware Graph Convolutional Network (RA-GCN), which directly embeds the regional relationships conveyed by a skeleton sequence into a temporal graph model. This innovation mitigates the deficiency of the original skeleton graph models. In addition, we strengthened the ability of the proposed channel modeling methods to extract multi-scale representations. These innovations result in a lightweight graph convolutional model, referred to as Graph2Net, that simultaneously extends the spatial and temporal perceptual fields, and thus enhances the capacity of the graph model to represent skeleton sequences. We conduct extensive experiments on NTU-RGB+D 60&120, Northwestern-UCLA, and Kinetics-400 datasets to show that our results surpass the performance of several mainstream methods while limiting the model complexity and computational overhead.
Heralded high-fidelity quantum hyper-CNOT gates assisted by charged quantum dots inside single-sided optical microcavities

Photonic hyper-parallel quantum information processing (QIP) can simplify the quantum circuit and improve the information-processing speed, as well as reduce the quantum resource consumption and suppress the photonic dissipation noise. Here, utilizing the singly charged semiconductor quantum dot (QD) inside single-sided optical microcavity as the potentially experimental platform, we propose five schemes for heralded four-qubit hyper-controlled-not (hyper-CNOT) gates, covering all cases of four-qubit hyper-CNOT gates operated on both the polarization and spatial-mode degrees of freedom (DoFs) of a two-photon system. The novel heralding mechanism improves the fidelity of each hyper-CNOT gate to unity in principle without the strict restriction of strong coupling. The adaptability and scalability of the schemes make the hyper-CNOT gates more accessible under current experimental technologies. These heralded high-fidelity photonic hyper-CNOT gates can therefore have immense utilization potentials in high-capacity quantum communication and fast quantum computing, which are of far-reaching significance for QIP.
The Mathematical Structure of Integrated Information Theory

Integrated Information Theory is one of the leading models of consciousness. It aims to describe both the quality and quantity of the conscious experience of a physical system, such as the brain, in a particular state. In this contribution, we propound the mathematical structure of the theory, separating the essentials from auxiliary formal tools. We provide a definition of a generalized IIT which has IIT 3.0 of Tononi et al., as well as the Quantum IIT introduced by Zanardi et al. as special cases. This provides an axiomatic definition of the theory which may serve as the starting point for future formal investigations and as an introduction suitable for researchers with a formal background.
Human Activity Recognition Using Wearable Sensors: Review, Challenges, Evaluation Benchmark

Recognizing human activity plays a significant role in the advancements of human-interaction applications in healthcare, personal fitness, and smart devices. Many papers presented various techniques for human activity representation that resulted in distinguishable progress. In this study, we conduct an extensive literature review on recent, top-performing techniques in human activity recognition based on wearable sensors. Due to the lack of standardized evaluation and to assess and ensure a fair comparison between the state-of-the-art techniques, we applied a standardized evaluation benchmark on the state-of-the-art techniques using six publicly available data-sets: MHealth, USCHAD, UTD-MHAD, WISDM, WHARF, and OPPORTUNITY. Also, we propose an experimental, improved approach that is a hybrid of enhanced handcrafted features and a neural network architecture which outperformed top-performing techniques with the same standardized evaluation benchmark applied concerning MHealth, USCHAD, UTD-MHAD data-sets.
SEMI: A Sequential Multi-Modal Information Transfer Network for E-Commerce Micro-Video Recommendations

The micro-video recommendation system becomes an essential part of the e-commerce platform, which helps disseminate micro-videos to potentially interested users. Existing micro-video recommendation methods only focus on users' browsing behaviors on micro-videos, but ignore their purchasing intentions in the e-commerce environment. Thus, they usually achieve unsatisfied e-commerce micro-video recommendation performances. To address this problem, we design a sequential multi-modal information transfer network (SEMI), which utilizes product-domain user behaviors to assist micro-video recommendations. SEMI effectively selects relevant items (i.e., micro-videos and products) with multi-modal features in the micro-video domain and product domain to characterize users' preferences. Moreover, we also propose a cross-domain contrastive learning (CCL) algorithm to pre-train sequence encoders for modeling users' sequential behaviors in these two domains. The objective of CCL is to maximize a lower bound of the mutual information between different domains. We have performed extensive experiments on a large-scale dataset collected from Taobao, a world-leading e-commerce platform. Experimental results show that the proposed method achieves significant improvements over state-of-the-art recommendation methods. Moreover, the proposed method has also been deployed on Taobao, and the online A/B testing results further demonstrate its practical value.
DT-DPoS: A Delegated Proof of Stake Consensus Algorithm with Dynamic Trust

Blockchain is a distributed accounting system. One of its key issues is how to ensure agreement between distrustful nodes. As an important component of blockchain, consensus algorithm can solve the consistency problem, and its efficiency directly determines the performance of blockchain. Compared with Proof of Work (PoW) and Proof of Stake (PoS), the existing Delegated Proof of Stake (DPoS) consensus algorithm improves the efficiency of consensus, but it will face some threats, such as DoS attack and collusion attack, because the mechanism that each witness node takes turns to generate blocks. Therefore, we propose a delegated proof of stake consensus algorithm with dynamic trust, that is, DT-DPoS. We improve an Eigen Trust-based trust model and construct an evaluation criterion based on the combination of stake voting and trust value, which improves the reliability of witness nodes and reduces the risk of collusion attacks. At the same time, we use a ring signature scheme, which ensures the privacy and anonymity of witness nodes. Finally, the theoretical analysis shows the effectiveness and scalability of our algorithm.
Towards a Blockchain-Based Multi-UAV Surveillance System

This study describes a blockchain-based multi-unmanned aerial vehicle (multi-UAV) surveillance framework that enables UAV coordination and financial exchange between system users. The objective of the system is to allow a set of Points-Of-Interest (POI) to be surveyed by a set of autonomous UAVs that cooperate to minimize the time between successive visits while exhibiting unpredictable behavior to prevent external agents from learning their movements. The system can be seen as a marketplace where the UAVs are the service providers and the POIs are the service seekers. This concept is based on a blockchain embedded on the UAVs and on some nodes on the ground, which has two main functionalities. The first one is to plan the route of each UAV through an efficient and computationally cheap game-theoretic decision algorithm implemented into a smart contract. The second one is to allow financial transactions between the system and its users, where the POIs subscribe to surveillance services by buying tokens. Conversely, the system pays the UAVs in tokens for the provided services. The first benchmarking experiments show that the IOTA blockchain is a potential blockchain candidate to be integrated in the UAV embedded system and that the chosen decentralized decision-making coordination strategy is efficient enough to fill the mission requirements while being computationally light.
Fused lasso for feature selection using structural information

Most state-of-the-art feature selection methods tend to overlook the structural relationship between a pair of samples associated with each feature dimension, which may encapsulate useful information for refining the performance of feature selection. Moreover, they usually consider candidate feature relevancy equivalent to selected feature relevancy, and therefore, some less relevant features may be misinterpreted as salient features. To overcome these issues, we propose a new feature selection method based on graph-based feature representations and the Fused Lasso framework in this paper. Unlike state-of-the-art feature selection approaches, our method has two main advantages. First, it can accommodate structural relationship between a pair of samples through a graph-based feature representation. Second, our method can enhance the trade-off between the relevancy of each individual feature on the one hand and its redundancy between pairwise features on the other. This is achieved through the use of a Fused Lasso framework applied to features reordered on the basis of their relevance with respect to the target feature. To effectively solve the optimization problem, an iterative algorithm is developed to identify the most discriminative features. Experiments demonstrate that our proposed approach can outperform its competitors on benchmark datasets.
New LCD MDS Codes of Non-Reed-Solomon Type

Both linear complementary dual (LCD) codes and maximum distance separable (MDS) codes have good algebraic structures, and they have interesting practical applications such as communication systems, data storage, quantum codes, and so on. So far, most of LCD MDS codes have been constructed by employing generalized Reed-Solomon codes. In this paper we construct some classes of new Euclidean LCD MDS codes and Hermitian LCD MDS codes which are not monomially equivalent to Reed-Solomon codes, called LCD MDS codes of non-Reed-Solomon type. Our method is based on the constructions of Beelen et al. (2017) and Roth and Lempel (1989). To the best of our knowledge, this is the first paper on the construction of LCD MDS codes of non-Reed-Solomon type; any LCD MDS code of non-Reed-Solomon type constructed by our method is not monomially equivalent to any LCD code constructed by the method of Carlet et al. (2018).
Energy Production Forecasting From Solar Photovoltaic Plants Based on Meteorological Parameters for Qassim Region, Saudi Arabia

Due to the increasing cost of crude oil because of pandemic COVID-19 and global environmental threats, the exploitation of fossil fuels for power generation is discouraged.Further, the demand for electrical power is increasing drastically, and therefore, the exploitation of renewable energy resources, particularly solar photovoltaic-based technology for power generation is invigorated.However, the large-scale penetration of solar photovoltaic is becoming a major challenge in terms of stability, reliability of power when integrated with the grid.Thus, it becomes important to develop a novel approach or strategy which is useful to improve power quality, reliability, and grid stability.Solar photovoltaic power forecasting is a key tool for this new era and becoming the main component for a smart grid environment.Here, in this paper, the ensemble trees approach-based machine learning approach is utilized to forecast the solar photovoltaic power with the help of various meteorological parameters.The high-quality measured data for meteorological parameters for Qassim, Saudi Arabia is used in this research.The performance of the proposed model is evaluated with the help of statistical indices such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE), Training Time (TT) and found within the desired limits.To validate the obtained results a comparative analysis with other machine learning models is carried out.Moreover, the proposed research may provide the roadmap in achieving the vision 2030 of the government of Saudi Arabia.
How Students Influence Faculty Satisfaction with Online Courses and Do the Age of Faculty Matter

Studying faculty satisfaction with online courses is important to the faculty’s well-being and manifests their productivity. Faculty satisfaction is affected by different factors. In this paper, the focus will be on students’ interactivity. Moreover, instructors' age was studied as a moderator to test whether it will negatively impact the mutual instructors-students relationship. Online surveys were collected from eleven different colleges in a university in UAE. The survey items were analyzed using correlation, R square tests, and Anova to test the study model in addition to the study hypotheses. The results have indicated that students’ interactivity significantly impacts faculty’s satisfaction with online courses. Age was found to have no impact on the instructors-students relationship; faculty’s age is just a number, affecting their satisfaction. However, the results denoted to a set of suggestions from the studied colleges that can be considered and applied by UAE universities.