Reviewerly/exHarmony huggingface dataset:
A Framework of the Critical Factors for Healthcare Providers to Share Data Securely Using Blockchain

The healthcare sector is suffering from inefficiencies in handling its data. Many patients and healthcare organisations are frustrated by the numerous hurdles to obtaining current, real-time patient information. Patients are also frustrated at trying to schedule appointments at health organisations that have outdated contact information. The healthcare sector’s attention has been drawn to blockchain technology as a part of the solution, especially since this technology has been successfully applied in the financial sector to improve the security of transactions. The aspect of interoperability is resolved adequately by blockchain technology, because it has the potential to store, manage and share EMRs safely in the healthcare community. Therefore, the technology is having a positive impact on healthcare outcomes for various stakeholders. Interoperability in healthcare eases the exchange of health-related data, such as EMRs, between healthcare entities so that records may be shared and distributed among clinical systems. To handle data in this sector without violating privacy is a challenge, whether in the collection, storage, or analysis. Poor security, which increases data breaches, endangers patients both mentally, socially, and financially. A lack of data-sharing in the healthcare sector is considered a significant issue worldwide. This research focuses on this gap by investigating the benefits of using blockchain at the Ministry of Health in Saudi Arabia, providing a detailed analysis of the healthcare sector, and evaluating how blockchain technology improves data-sharing security. This research proposes a framework that identifies the factors supporting data-sharing using blockchain among healthcare organisations. It has three categories: healthcare systems factors; security factors; and blockchain factors. A triangulation technique achieved reliable results in three steps: a literature review; an expert review; and a questionnaire. This gave a comprehensive picture of the research topic, validating and confirming the results. To construct the framework, factors were comprehensively extracted from the literature then analysed, cleared of duplicates, and categorised. As a result, the final framework is confirmed as being based on the literature and expert review, and it is supported by the practitioners’ survey.
Scalable Representation Learning for Dynamic Heterogeneous Information Networks via Metagraphs

Content representation is a fundamental task in information retrieval. Representation learning is aimed at capturing features of an information object in a low-dimensional space. Most research on representation learning for heterogeneous information networks (HINs) focuses on static HINs. In practice, however, networks are dynamic and subject to constant change. In this article, we propose a novel and scalable representation learning model, M-DHIN , to explore the evolution of a dynamic HIN. We regard a dynamic HIN as a series of snapshots with different time stamps. We first use a static embedding method to learn the initial embeddings of a dynamic HIN at the first time stamp. We describe the features of the initial HIN via metagraphs, which retains more structural and semantic information than traditional path-oriented static models. We also adopt a complex embedding scheme to better distinguish between symmetric and asymmetric metagraphs. Unlike traditional models that process an entire network at each time stamp, we build a so-called change dataset that only includes nodes involved in a triadic closure or opening process, as well as newly added or deleted nodes. Then, we utilize the above metagraph-based mechanism to train on the change dataset. As a result of this setup, M-DHIN is scalable to large dynamic HINs since it only needs to model the entire HIN once while only the changed parts need to be processed over time. Existing dynamic embedding models only express the existing snapshots and cannot predict the future network structure. To equip M-DHIN with this ability, we introduce an LSTM-based deep autoencoder model that processes the evolution of the graph via an LSTM encoder and outputs the predicted graph. Finally, we evaluate the proposed model, M-DHIN , on real-life datasets and demonstrate that it significantly and consistently outperforms state-of-the-art models.
BEAT: A Large-Scale Semantic and Emotional Multi-modal Dataset for Conversational Gestures Synthesis

Achieving realistic, vivid, and human-like synthesized conversational gestures conditioned on multi-modal data is still an unsolved problem due to the lack of available datasets, models and standard evaluation metrics. To address this, we build Body-Expression-Audio-Text dataset, BEAT, which has i) 76 h, high-quality, multi-modal data captured from 30 speakers talking with eight different emotions and in four different languages, ii) 32 millions frame-level emotion and semantic relevance annotations. Our statistical analysis on BEAT demonstrates the correlation of conversational gestures with facial expressions, emotions, and semantics, in addition to the known correlation with audio, text, and speaker identity. Based on this observation, we propose a baseline model, Cascaded Motion Network (CaMN), which consists of above six modalities modeled in a cascaded architecture for gesture synthesis. To evaluate the semantic relevancy, we introduce a metric, Semantic Relevance Gesture Recall (SRGR). Qualitative and quantitative experiments demonstrate metrics’ validness, ground truth data quality, and baseline’s state-of-the-art performance. To the best of our knowledge, BEAT is the largest motion capture dataset for investigating human gestures, which may contribute to a number of different research fields, including controllable gesture synthesis, cross-modality analysis, and emotional gesture recognition. The data, code and model are available on https://pantomatrix.github.io/BEAT/ .
Bi-directional Object-Context Prioritization Learning for Saliency Ranking

The saliency ranking task is recently proposed to study the visual behavior that humans would typically shift their attention over different objects of a scene based on their degrees of saliency. Existing approaches focus on learning either object-object or object-scene relations. Such a strategy follows the idea of object-based attention in Psychology, but it tends to favor objects with strong semantics (e.g., humans), resulting in unrealistic saliency ranking. We observe that spatial attention works concurrently with object-based attention in the human visual recognition system. During the recognition process, the human spatial attention mechanism would move, engage, and disengage from region to region (i.e., context to context). This inspires us to model region-level interactions, in addition to object-level reasoning, for saliency ranking. Hence, we propose a novel bi-directional method to unify spatial attention and object-based attention for saliency ranking. Our model has two novel modules: (1) a selective object saliency (SOS) module to model object-based attention via inferring the semantic representation of salient objects, and (2) an object-context-object relation (OCOR) module to allocate saliency ranks to objects by jointly modeling object-context and context-object interactions of salient objects. Extensive experiments show that our approach outperforms existing state-of-the-art methods. Code and pretrained model are available at https://github.com/GrassBro/OCOR.
Effect of Render Resolution on Gameplay Experience, Performance, and Simulator Sickness in Virtual Reality Games

Higher resolution is one of the main directions and drivers in the development of virtual reality (VR) head-mounted displays (HMDs). However, given its associated higher cost, it is important to determine the benefits of having higher resolution on user experience. For non-VR games, higher resolution is often thought to lead to a better experience, but it is unexplored in VR games. This research aims to investigate the resolution tradeoff in gameplay experience, performance, and simulator sickness (SS) for VR games, particularly first-person shooter (FPS) games. To this end, we designed an experiment to collect gameplay experience, SS, and player performance data with a popular VR FPS game, Half-Life: Alyx. Our results indicate that 2K resolution is an important threshold for an enhanced gameplay experience without affecting performance and increasing SS levels. Moreover, the resolution from 1K to 4K has no significant difference in player performance. Our results can inform game developers and players in determining the type of HMD they want to use to balance the tradeoff between costs and benefits and achieve a more optimal experience.
Industrially microfabricated ion trap with 1 eV trap depth

Abstract Scaling trapped-ion quantum computing will require robust trapping of at least hundreds of ions over long periods, while increasing the complexity and functionality of the trap itself. Symmetric three-dimensional (3D) structures enable high trap depth, but microfabrication techniques are generally better suited to planar structures that produce less ideal conditions for trapping. We present an ion trap fabricated on stacked eight-inch wafers in a large-scale micro-electro-mechanical system microfabrication process that provides reproducible traps at a large volume. Electrodes are patterned on the surfaces of two opposing wafers bonded to a spacer, forming a 3D structure with 2.5 μ m standard deviation in alignment across the stack. We implement a design achieving a trap depth of 1 eV for a 40 Ca + ion held at 200 μ m from either electrode plane. We characterize traps, achieving measurement agreement with simulations to within ±5% for mode frequencies spanning 0.6–3.8 MHz, and evaluate stray electric field across multiple trapping sites. We measure motional heating rates over an extensive range of trap frequencies, and temperatures, observing 40 phonons/s at 1 MHz and 185 K. This fabrication method provides a highly scalable approach for producing a new generation of 3D ion traps.
SOTitle: A Transformer-based Post Title Generation Approach for Stack Overflow

On Stack Overflow, developers can not only browse question posts to solve their programming problems but also gain expertise from the question posts to help improve their programming skills. Therefore, improving the quality of question posts in Stack Overflow has attracted the wide attention of researchers. A concise and precise title can play an important role in helping developers understand the key information of the question post, which can improve the post quality. How-ever, the quality of the generated title is not high due to the lack of professional knowledge related to their questions or the poor presentation ability of developers. A previous study aimed to automatically generate the title by analyzing the code snippets in the question post. However, this study ignored the useful information in the corresponding problem description. Therefore, we propose an approach SOTitle for automatic post title generation by leveraging the code snippets and the problem description in the question post (i.e., the multi-modal input). SOTitle follows the Transformer structure, which can effectively capture long-term dependencies through a multi-head attention mechanism. To verify the effectiveness of SOTitle, we construct a large-scale high-quality corpus from Stack Overflow, which includes 1,168,257 high-quality question posts for four popular programming languages. Experimental results show that SOTitle can significantly outperform six state-of-the-art baselines in both automatic evaluation and human evaluation. To encourage follow-up studies, we make our corpus and approach publicly available.
Swin-Pose: Swin Transformer Based Human Pose Estimation

Convolutional neural networks (CNNs) have been widely utilized in many computer vision tasks. However, CNNs have a fixed reception field and lack the ability of long-range perception, which is crucial to human pose estimation. Transformer architecture has been adopted to computer vision applications recently and is proven to be a highly effective architecture. We are interested in exploring its capability in human pose estimation, and thus propose a novel model based on transformer, enhanced with a feature pyramid fusion structure. More specifically, we use pre-trained Swin Transformer to extract features, and leverage a feature pyramid structure to extract and fuse feature maps from different stages. The experiment results of our study have demonstrated that the proposed transformer-based model can achieve better performance compared to the state-of-the-art CNN-based models.
Wireless Resource Management in Intelligent Semantic Communication Networks

The prosperity of artificial intelligence (AI) has laid a promising paradigm of communication system, i.e., intelligent semantic communication (ISC), where semantic contents, instead of traditional bit sequences, are coded by AI models for efficient communication. Due to the unique demand of background knowledge for semantic recovery, wireless resource management faces new challenges in ISC. In this paper, we address the user association (UA) and bandwidth allocation (BA) problems in an ISC-enabled heterogeneous network (ISC-HetNet). We first introduce the auxiliary knowledge base (KB) into the system model, and develop a new performance metric for the ISC-HetNet, named system throughput in message (STM). Joint optimization of UA and BA is then formulated with the aim of STM maximization subject to KB matching and wireless bandwidth constraints. To this end, we propose a two-stage solution, including a stochastic programming method in the first stage to obtain a deterministic objective with semantic confidence, and a heuristic algorithm in the second stage to reach the optimality of UA and BA. Numerical results show great superiority and reliability of our proposed solution on the STM performance when compared with two baseline algorithms.
Quantum support vector machine based on regularized Newton method

An elegant quantum version of least-square support vector machine, which is exponentially faster than the classical counterpart, was given by Rebentrost et al. using the matrix inversion algorithm (HHL). However, the application of the HHL algorithm is restricted when the structure of the input matrix is not well. The iteration algorithms such as the Newton method are widespread in training the classical support vector machine. This paper demonstrates a quantum support vector machine based on the regularized Newton method (RN-QSVM), which achieves an exponential speed-up over classical algorithm. At first, the regularized quantum Newton algorithm is proposed to get rid of the constraint of input matrix. Then we train the RN-QSVM by using the regularized quantum Newton algorithm and classify a query sample by constructing the quantum state. Experiments demonstrate that RN-QSVM respectively provides advantages in terms of accuracy, robustness, and complexity compared to QSLS-SVM, LS-QSVM, and the classical method.
Virtual and Augmented Reality in the Disaster Management Technology: A Literature Review of the Past 11 years

This study presents a systematic review of the literature on virtual reality (VR), augmented reality (AR) and Mixed Reality (MR) used in disaster management. We consider the factors such as publication type, publication year, application domain, and technology used. We surveyed papers from 2009 to 2019 available in the Web of Science and Google Scholar database, and 84 research articles were selected for the review study. After an extensive review of the literature, it was found that the XR technology is applied extensively in computer simulation modeling, interaction techniques, training, infrastructure assessment and reconnaissance, and public awareness areas of disaster management. We found diverse advantages, opportunities, and challenges of XR usage for disaster management, which are discussed in detail. Furthermore, current research gaps in the field of XR technology for disaster management technology, which are needed to better support disaster management, are identified and discussed in an effort to provide direction to the future research.
MACPABE: Multi‐Authority‐based CP‐ABE with efficient attribute revocation for IoT‐enabled healthcare infrastructure

Abstract The Internet of Things (IoT) technology along with cloud computing has gained much attention in recent years for its potential to upgrade conventional healthcare systems. Outsourcing healthcare data to a cloud environment from IoT devices is very essential as IoT devices are lightweight. To maintain confidentiality and to achieve fine‐grained access control, the ciphertext policy attribute‐based encryption (CP‐ABE) technique is utilized very often in an IoT‐based healthcare system for encrypting patients' healthcare data. However, an attribute revocation may affect the other users with the same attribute set, as well as the entire system due to its security concerns. This paper proposes a novel CP‐ABE‐based fine‐grained access control scheme to solve the attribute revocation problem. The proposed technique includes multiple attribute authorities to reduce the work overhead of having a single authority in the traditional CP‐ABE systems. In addition, the proposed scheme outsources the decryption process to a decryption assistant entity to reduce the decryption overhead of the end‐users. To prove the efficiency of the proposed scheme, both formal security analysis and performance comparisons are presented in this paper. Results and discussion prove the effectiveness of the proposed scheme over some well‐known schemes.
Taxonomy of Load Balancing Practices in the Cloud Computing Paradigm

Rapid growth in communication technology allows users location-independent access to IT infrastructure at pay-per-use via cloud computing. This has paved a new paradigm in information processing for the consumers. Due to Cloud's inherent characteristics, most service providers shift to the Cloud and its data centers. To retain Cloud's services' reliability, it's essential to carry out the minimum latency tasks and cost-effectively. Various techniques to improve performance and use of assets are focused on load control, task management, resource management, service quality, and workload management. Data load balancing helps data centers to avoid overload/underload of virtual machines, a difficulty in the world of cloud computing. This study reports a state-of-the-art analysis of current load balancing approaches', problems, and complexities to design more successful algorithms.
Integrated Internet of Things with Blockchains for Vendor-Management Inventory System

In recent era, the supply chain management is becoming a complex valued network that offers a major competitive advantage over logistics and supply chain management. Despite its advantages, the complexity is becoming a challenge that should provide verification of sources, maintaining product visibility and monitoring while moving via supply chains. The adoption of Internet of Things (IoT) can support all these movements that tends to track and monitor the activities of the products moving in the chain network. Such optimization enables the operations to get optimized that includes manufacturing, warehousing and transportation. In addition, the transparency of the supply chains can be maintained via blockchain and when combined with IoT, it increases the effectiveness and efficacy of the supply chain
A Lyapunov-stability-based context-layered recurrent pi-sigma neural network for the identification of nonlinear systems

A novel higher-order context-layered recurrent pi-sigma neural network (CLRPSNN) is presented for the identification of nonlinear dynamical systems. The proposed model is the modified form of the classical pi-sigma neural network (PSNN) and contains an additional layer (known as the context layer) of the context nodes. Pi-sigma networks involve a product operator/unit in their output layer which indirectly incorporates in them the capability of higher-order networks and also reduces their network complexity. For tuning the weights of the proposed CLRPSNN model, a learning procedure is developed by combining the Back-Propagation (BP) and Lyapunov-stability method. The performance of the proposed model is compared with other models such as PSNN, Feed-forward neural network (FFNN) (containing single hidden layer), and various popular recurrent neural network (RNN) like Elman recurrent neural network (ERNN), Jordan recurrent neural network (JRNN), Diagonal recurrent neural network (DRNN), and a deep neural network (DNN). The simulation study showed that the proposed model has given better results as compared to the other models.
Hybrid Sparrow Clustered (HSC) Algorithm for Top-N Recommendation System

Recommendation systems suggest relevant items to a user based on the similarity between users or between items. In a collaborative filtering approach for generating recommendations, there is a symmetry between the users. That is, if user A has similar interests with user B, then an item liked by B can be recommended to A and vice versa. To provide optimal and fast recommendations, a recommender system may generate and keep clusters of existing users/items. In this research work, a hybrid sparrow clustered (HSC) recommender system is developed, and is applied to the MovieLens dataset to demonstrate its effectiveness and efficiency. The proposed method (HSC) is also compared to other methods, and the results are compared. Precision, mean absolute error, recall, and accuracy metrics were used to figure out how well the movie recommender system worked for the HSC collaborative movie recommender system. The results of the experiment on the MovieLens dataset show that the proposed method is quite promising when it comes to scalability, performance, and personalized movie recommendations.
Fair active learning

Machine learning (ML) is increasingly being used in high-stakes applications impacting society. Therefore, it is of critical importance that ML models do not propagate discrimination. Collecting accurate labeled data in societal applications is challenging and costly. Active learning is a promising approach to build an accurate classifier by interactively querying an oracle within a labeling budget. We introduce the fair active learning framework to carefully select data points to be labeled so as to balance model accuracy and fairness. To incorporate the notion of fairness in the active learning sampling core, it is required to measure the fairness of the model after adding each unlabeled sample. Since their labels are unknown in advance, we propose an expected fairness metric to probabilistically measure the impact of each sample if added for each possible class label. Next, we propose multiple optimizations to balance the trade-off between accuracy and fairness. Our first optimization linearly aggregate the expected fairness with entropy using a control parameter. To avoid erroneous estimation of the expected fairness, we propose a nested approach to maintain the accuracy of the model, limiting the search space to the top bucket of sample points with large entropy. Finally, to ensure the unfairness reduction of the model after labeling, we propose to replicate the points that truly reduce the unfairness after labeling. We demonstrate the effectiveness and efficiency of our proposed algorithms over widely used benchmark datasets using demographic parity and equalized odds notions of fairness.