Reviewerly/exHarmony huggingface dataset:
Blockchain and Internet of Things in Healthcare Systems

The rapid development in information technologies is making a big change to the health sector. The method of prescription, diagnosis and sharing of vital health information is still attached to conventional paper methods, thus reducing the efficiency of health workers, and causing the lack of a robust database for medical professionals who can handle specialized diagnostics activities for special patients. Therefore, it is necessary to introduce digital health transformation technologies like blockchain and the Internet of things (IoT) in the healthcare system. The analysis of blockchain components is required to develop a new layer that adapts to the architecture of a medical centre to integrate this technology in a hospital. This assures that the data is kept cryptographically private to avoid exposure and that the entire procedure is double-checked by many blocks. Therefore, this chapter focus on the applicability of IoT-based blockchain in the healthcare system. The issues and challenges of these technologies are inventively discussed and identifies the prospects of IoT-based enabled with blockchain in the healthcare sector. Lastly, the investigation disputes in IoT-based enabled blockchain framework were discussed with a proposed framework for the application of blockchain-enabled IoT-based system. The real-time application of the proposed framework will enhance the security and privacy of healthcare systems. The advent of blockchain-enabled IoT-based systems in healthcare will assist the launching of secure digital intelligent systems, which will help to revolutionize the sector and increase the management of patients and health professionals.
Optimized hybrid RNN model for human activity recognition in untrimmed video

Human activity recognition is a field of video processing that requires restricted temporal analysis of video sequences for estimating the existence of different human actions. Designing an efficient human activity model requires credible implementations of keyframe extraction, preprocessing, feature extraction and selection, classification, and pattern recognition methods. In the real-time video, sequences are untrimmed and do not have any activity endpoints for effective recognition. Thus, we propose a hybrid gated recurrent unit and long short-term memory-based recurrent neural network model for high-efficiency human action recognition in untrimmed video datasets. The proposed model is tested on the TRECVID dataset, along with other online datasets, and is observed to have an accuracy of over 91% for untrimmed video-based activity recognition. This accuracy is compared with various state-of-the-art models and is found to be higher when evaluated on multiple datasets.
Relational POI recommendation model combined with geographic information

Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.
Understanding the Effects of Mixed Reality on Video Game Satisfaction, Enjoyment, and Performance

Background With the rising demand for the use and application of modern immersive technologies, recent studies have investigated the user experience of augmented reality (AR) and virtual reality (VR) for video game purposes. Despite AR's and VR's pervasiveness in the video game industry, research studies into the effects of mixed reality (MR) on video game experience are scarce. This study examined the impact of MR on video game satisfaction, enjoyment, and user performance of first-time users. Method Participants played the same strategy video game across two platforms, an MR headset and a mobile device. A short version of the Game User Experience Satisfaction Scale (GUESS-18) and the Enjoyment scale (ENJOY) were used to measure satisfaction and enjoyment. Results Results demonstrated that MR provided greater overall satisfaction, engrossment, creativity, and personal gratification. In addition, results revealed higher overall enjoyment, challenge/improvement, and engagement in the MR condition. Interestingly, participants performed better with the mobile version. No statistical differences were found between the conditions regarding the GUESS's usability, audio aesthetics, visual aesthetics, and the ENJOY's pleasure and competence. Conclusion The study provides insight into how MR influences satisfaction and enjoyment for video games. Playing a video game in mixed reality has the potential to enhance the user experience of game players despite the possibility of simulator sickness and worse performance than traditional mobile environments. Features of the gameplay that enhanced as well as diminished video game experience are discussed.
WGLFNets: Wavelet-based global–local filtering networks for image denoising with structure preservation

Efficiency and denoising performance are two important indexes to objectively evaluate an image denoising method. Traditional model-based denoising methods usually pursue pleasing performance at the cost of highly computational complexity, while the popular deep learning-based methods perform well on efficiency improvement but still fail in rich structure preservation. To cope with these problems, this paper proposed a novel wavelet-based deep denoising methods, called as wavelet-based global–local filtering networks (WGLFNets). The WGLFNets includes the following three key points: First, an noisy image is decomposed into a base image and three-directional detail images by employing discrete wavelet transform (DWT). Second, the base component is denoised by a global denoising network while the detail components are recovered by a local detail restoration network. Finally, all processed components are utilized to construct the high-quality image by the inverse DWT (IDWT). Experimental results demonstrate that the WGLFNets is effective and is superior to the state-of-the-arts.
Multi-receiver signcryption scheme with multiple key generation centers through public channel in edge computing

The emerging edge computing technology for the Internet of Things has been playing an important role in our daily life. It is promising to utilize a multi-receiver signcryption scheme to protect the transmission data when an edge device broadcasts its sensing data to many different end devices at a time. There are several things to consider when we design a signcryption scheme. First existing schemes need to maintain a secure channel to generate the user private key, which may increase economic costs. Second the system private key of those schemes is kept secret by a single key generation center (KGC), and the single point of failure of KGC may compromise the whole system. For this, we propose a multi-receiver multi-message signcryption scheme without the secure channel. Firstly the scheme allows KGC to send secrets through the public channel, which reduces maintenance costs. Secondly, to eliminate the single point of failure, the scheme utilizes multiple KGCs to manage the system private key, and updates the secret of each KGC periodically to resist advanced persistent threat attacks. We demonstrate that the proposed scheme can achieve expected security properties. Performance analysis shows that it is with shorter ciphertext length and higher efficiency.
On theoretical justification of the forward–backward algorithm for the variational learning of Bayesian hidden Markov models

In the variational learning process of a Bayesian Hidden Markov model, the forward-backward algorithm is heuristically applied without theoretical justification. This is potentially problematic, because the original derivation of the forward-backward algorithm implicitly requires the parameters to be normalized, which does not hold in the variational learning process of Bayesian HMM. In this paper, we prove that such a requirement is not necessary for the forward-backward algorithm to obtain the correct result. We prove the result from two perspectives. The first proof straightforwardly verifies that implementing the forward-backward algorithm with the unnormalised parameters is equivalent to implementing it with the normalized parameters. The second proof provides a new derivation of the forward-backward algorithm without hidden Markov assumptions and probabilistic meanings of the parameters. As a result, we justify that applying the forward-backward algorithm is theoretically correct and reasonable in the variational learning of Bayesian hidden Markov models.
An Advanced Deep Attention Collaborative Mechanism for Secure Educational Email Services

The COVID-19 crisis has once again highlighted the vulnerabilities of some critical areas in cyberspace, especially in the field of education, as distance learning and social distance have increased their dependence on digital technologies and connectivity. Many recent cyberattacks on e-learning systems, educational content services, and trainee management systems have created severe demands for specialized technological solutions to protect the security of modern training methods. Email is one of the most critical technologies of educational organizations that are attacked daily by spam, phishing campaigns, and all kinds of malicious programs. Considering the efforts made by the global research community to ensure educational processes, this study presents an advanced deep attention collaborative filter for secure academic email services. It is a specialized application of intelligent techniques that, for the first time, examines and models the problem of spam as a system of graphs where collaborative referral systems undertake the processing and analysis of direct and indirect social information to detect and categorize spam emails. In this study, nonnegative matrix factorization (NMF) is applied to the social graph adjacent table to place users in one (or more) overlapping communities. Also, using a deep attention mechanism, it becomes personalized for each user. At the same time, with the introduction of exponential random graph models (ERGMs) in the process of factorization, local dependencies are significantly mitigated to achieve the revelation of malicious communities. This methodology is being tested successfully in implementing mail protection systems for educational organizations. According to the findings, the proposed algorithm outperforms all other compared algorithms in every metric tested.
The Real Estate Transaction Trace System Model Based on Ethereum Blockchain Platform

Blockchain is one of the leading 4.0 technologies because of its potential applications in many fields, such as real estate. Real estate is an old market and little changes. Therefore, buying and selling real estate brings many risks because it is difficult to find and verify information. Real estate transactions are complex, time-consuming and have high intermediary fees. However, Blockchain technology has opened up many ways to change this. Therefore, we study Blockchain technology and propose Real Estate Transaction Trace (RETT) system model to change and improve the way real estate transactions work in Vietnam. The RETT system model is capable of managing and tracking the entire process of real estate transactions of all participants (owners, buyers, sellers, notaries). The government or real estate investor consortium can easily manage and trace all the purchase or lease transaction history of a property (land, town-house, apartment). We designed Blockchain network architecture, distributed ledger and smart contract. As a result, our method can digitize assets on the Blockchain, store decentralized transaction history, enable encryption, and direct transactions between sellers and buyers. In addition, our system model can reduce the problem of data explosion, execute multiple transactions at the same time, prevent data tampering and sensitive information disclosure. We have built a prototype of this system model based on the Ethereum Blockchain platform. Thereby, we have proven the effectiveness of RETT system model and practical applicability through experimental transactions. Our method improves transparency, eliminates intermediaries, saves costs and increases mutual trust in real estate transactions. Our system model can manage real estate transactions comprehensively and can be able to overcome the weaknesses of the current real estate transaction management system according to the Client-Server model. Therefore, our system model can be deployed in the smart city to manage and trace the entire real estate transaction.
Physicalizing Sustainable Development Goals Data: An Example with SDG 7 (Affordable and Clean Energy)

In 2015, all members of the United Nations adopted the 2030 agenda for Sustainable Development (SD). Seventeen (17) goals (SDGs) were formulated towards peace and prosperity for people and the planet. However, concerns have been raised about whether these sustainable goals can be achieved by 2030. Communicating advances regarding SDGs to citizens in an effective and engaging way is thus crucial, as it can reveal which goals need more attention and prompt them to think about what to do at an individual level to support SDG progress. Physicalizations present an opportunity in this context and the overall goal of this work is to empirically articulate the merits of different strategies to convey SDG data physically. We created a data physicalization that uses vibration and temperature as modalities to convey facts related to SDG 7 (Affordable and Clean Energy). Vibration and temperature was chosen to aim for reducing the metaphorical distance between the data and the representation (i.e. to align the quality of data with quality of representation). In a preliminary evaluation, both modalities were perceived as enjoyable by the participants. The two modalities were efficient, however, vibration as a modality was more effective. Temperature, despite presenting a lower metaphorical distance, did not appear to be an effective modality to convey SDG information.
An Exploration to the Quantum Cryptography Technology

The security of our data is the prime priority as it is said &#x201C;Data is the new Oil&#x201D;. Nowadays, most of our communications are either recorded or forged. There are algorithms used under classical encryption, such as Rivest-Shamir-Adleman (RSA), digital signature, elliptic-curve cryptography (ECC), and more, to protect our communication and data. However, these algorithms are breakable with the help of Quantum Cryptography. In addition, this technology provides the most secure form of communication between entities under the fundamental law of Physics. Here, we are abiding to discuss the term &#x201C;Quantum Cryptography.&#x201D; The aim of this paper is to explore the knowledge related to the Quantum Cryptography, Quantum Key Distribution; and their elements, implementation, and the latest research. Moreover, exploration of the loopholes and the security of Internet of Things (IoT) infrastructure and current used classical cryptographic algorithms are described in the paper.
Safe RuleFit: Learning Optimal Sparse Rule Model by Meta Safe Screening

We consider the problem of learning a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">sparse rule model</i> , a prediction model in the form of a sparse linear combination of rules, where a rule is an indicator function defined over a hyper-rectangle in the input space. Since the number of all possible such rules is extremely large, it has been computationally intractable to select the optimal set of active rules. In this paper, to solve this difficulty for learning the optimal sparse rule model, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Safe RuleFit (SRF)</i> . Our basic idea is to develop <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">meta safe screening (mSS)</i> , which is a non-trivial extension of well-known safe screening (SS) techniques. While SS is used for screening out one feature, mSS can be used for screening out multiple features by exploiting the inclusion-relations of hyper-rectangles in the input space. SRF provides a general framework for fitting sparse rule models for regression and classification, and it can be extended to handle more general sparse regularizations such as group regularization. We demonstrate the advantages of SRF through intensive numerical experiments.
A Study on the Mental Models of Users Concerning Existing Software

Context and Motivation: Software users describe requirements for new software and give feedback to existing software. Both are well studied in requirements engineering research. However, both are also heavily influenced by the users’ comprehension of existing software. We do not know which aspects of software users have in mind when they talk about it. While their mental model is interesting in itself, knowing this mental model could be helpful both, during requirements elicitation and validation-whenever user statements need to be understood. Problem: There is no standard methodology to study mental models and existing mental model studies mostly focus on specific elements of a specific software. Principal results: We have asked students to describe and draw a certain software. We coded the answers to understand the abstraction levels and the software aspects mentioned. We also analyzed differences. Our results showed a strong focus on the interaction and domain level. The users’ drawings primarily represented the user interface. We found only small differences between participants with a computer science background compared to those without one. Contribution: This paper presents initial insights on the software aspects in the mental model of users concerning existing software. It also describes our method to study this model and ideas for future studies.
Depth and Video Segmentation Based Visual Attention for Embodied Question Answering

Embodied Question Answering (EQA) is a newly defined research area where an agent is required to answer the user's questions by exploring the real-world environment. It has attracted increasing research interests due to its broad applications in personal assistants and in-home robots. Most of the existing methods perform poorly in terms of answering and navigation accuracy due to the absence of fine-level semantic information, stability to the ambiguity, and 3D spatial information of the virtual environment. To tackle these problems, we propose a depth and segmentation based visual attention mechanism for Embodied Question Answering. First, we extract local semantic features by introducing a novel high-speed video segmentation framework. Then guided by the extracted semantic features, a depth and segmentation based visual attention mechanism is proposed for the Visual Question Answering (VQA) sub-task. Further, a feature fusion strategy is designed to guide the navigator's training process without much additional computational cost. The ablation experiments show that our method effectively boosts the performance of the VQA module and navigation module, leading to 4.9 % and 5.6 % overall improvement in EQA accuracy on House3D and Matterport3D datasets respectively.
A quantum computing simulator scheme using MPI technology on cloud platform

With the deeply studied of quantum computing by research fellows, the computational complexity and time complexity of quantum algorithms gradually increase. The quantum computing simulator provides an effective platform for the development and verification of quantum algorithms. However, the running time and memory overhead during calculation will increase exponentially with the number of qubits. Therefore, the difficulty of high-speed quantum computing on classical computers will gradually increase. At present, a single node has been unable to meet the resource requirements of the algorithm in the quantum computing process, and it is necessary to expand the memory resources in the multi-node computing cluster. Message passing interface has high performance inter-process communication capabilities, which can balance the data transmission between different hosts. Quantum computing tasks can be distributed on several servers to work together to solve the same problem. Therefore, this paper designs an advanced scheme of deploying quantum computing programs on cloud computing platform to support message exchanging between servers using message passing interface to accelerate quantum computing.
Presence, flow, and narrative absorption questionnaires: a scoping review

Background: This is a review and analysis of the questionnaires most used in empirical research on psychological phenomena labelled as "presence," "flow," and "narrative absorption," mostly for experiences mediated by technology (printed books, screens for games and films, and virtual reality). Overlapping concepts have been formulated in different fields according to specific disciplinary interests and based on knowledge within each field. Objectives: This review focuses on how language is actually used in questionnaire items, rather than on how concepts are formulated top-down and associated with corresponding linguistic expressions that become items of a questionnaire. The goal is to highlight similarities and overlaps in order to show a possible interdisciplinary agreement about the core aspects of the psychological states elicited by mediated experiences. Eligibility criteria: Questionnaires developed or used for research about VR, video games, films, or books have been selected for analysis. They should be available in English and used in empirical research since the year 2000. Sources of evidence: A search has been performed through Google Scholar and two other disciplinary bibliographies edited by international learned societies. Charting methods: The items of each questionnaire are categorized based on their wordings, and thus independently from the conceptual models within which they have been developed. Based on this categorization, various domains to which the items can be ascribed are identified (e.g. space, realism, agency, etc.) and psychological phenomena are linked to them (e.g. presence, social presence, narrative absorption, etc.). Results: 308 items in 23 questionnaires have been found to have overlapping of wordings. Conclusions: A list of the core aspects of presence, social presence, flow, and narrative absorption is presented, together with a critical selection of items suitable to measure each construct.