Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Internet-of-Things for Secure Transmission of Medical Data in Rural Areas

Abstract Patients’ medical data are extremely sensitive information during storage and transfer, and it needs the highest security level. Furthermore, these records must frequently be linked to patient medical data, and then the linked medical data are securely transmitted to the healthcare center. In this study, a Blockchain-Based Traceable Data Sharing method is proposed to securely transfer the medical data. A Paillier homomorphic encryption method is used to prevent data theft or attacks from occurring in the cloud as a result of the transfer of medical data there. It prevents intravenous third parties, which executes arithmetic operations on the ciphertext. Then the encrypted data are stored in the cloud and to remove clone nodes in the gateway, a software-defined networking technology is introduced. Then a Blockchain-Based Traceable Data Sharing is proposed to ensure data privacy and authenticity while maintaining data privacy at the point of data transmission. Data are then encrypted using a new Enhanced Cipher Text-Policy Encryption Attribute-based Encryption (E-CP-ABE). Private blockchain transfers are carried out on the chain, supporting fine-grain access control with flexible access policies and creating a private key in E-CP-ABE. The presented technique is executed in Matlab software of version R2020a. The performance parameters are encryption, and decryption time, mean square error (MSE), peak signal-to-noise ratio (PSNR), sensitivity, respectively. The encryption process function is nearly 8% superior than the existing methods and the decryption time is 14% greater than other methods. As a result, this study shows that the research approach outperformed in terms of encryption time and decryption time, as well as PSNR, MSE, and sensitivity. This technique outperforms other state-of-the-art algorithms in terms of imperceptibility and robustness against various attacks. Consequently, this approach is more reliable than previous methods for the transmission of medical data.
Sequence neural network for recommendation with multi-feature fusion

Recommender systems have become a pervasive and effectual means to cope with the boosted complexity of decision-making process resulting from information overload. The key factor confronting recommendation tasks is to accurately characterize user preferences which are not only intrinsically dynamic and evolving, but also associated with multiple features and contexts. Sequential models especially Recurrent Neural Networks (RNNs) have become a powerful tool to address the issue. However, most of current RNN methods select the sequence of items that the user interacted with in the past to represent the current user state without considering contextual information associated with users and items simultaneously. Besides, the existing methods are hard to access and store the importance information of user representation. In this paper, we put forward two types of Gated Recurrent Units (GRU) models, i.e., multi-feature linear fusion GRU and multi-feature user integration GRU, to address the above drawbacks and thus help recommend more personalized next items to users. The proposed models employ multi-feature fusion technology including feature concatenation and one-dimensional Convolutional Neural Network (1D CNN) to generate input of GRU for learning more feature interaction information. Experiments on two real-world datasets demonstrate the performance gains of the proposed model compared with state-of-the-art. Further ablation studies verify the impact of different features for two models. • A linear GRU with multi-feature fusion based on contextual information is proposed. • A new multi-feature user integration GRU to filter user information is proposed. • The TextCNN is used to handle the diversity of text data in the datasets. • Ablation studies verify the impact of different features. • Our methods outperform competitive methods in terms of MRR, Recall and F1.
Recompilation-enhanced simulation of electron–phonon dynamics on IBM quantum computers

Simulating quantum systems is believed to be one of the first applications for which quantum computers may demonstrate a useful advantage. For many problems in physics, we are interested in studying the evolution of the electron-phonon Hamiltonian, for which efficient digital quantum computing schemes exist. Yet to date, no accurate simulation of this system has been produced on real quantum hardware. In this work, we consider the absolute resource cost for gate-based quantum simulation of small electron-phonon systems as dictated by the number of Trotter steps and bosonic energy levels necessary for the convergence of dynamics. We then apply these findings to perform experiments on IBM quantum hardware for both weak and strong electron-phonon coupling. Despite significant device noise, through the use of approximate circuit recompilation we obtain electron-phonon dynamics on current quantum computers comparable to exact diagonalisation. Our results represent a significant step in utilising near term quantum computers for simulation of quantum dynamics and highlight the novelty of approximate circuit recompilation as a tool for reducing noise.
A novel framework for image-to-image translation and image compression

Data-driven paradigms using machine learning are becoming ubiquitous in image processing and communications. In particular, image-to-image (I2I) translation is a generic and widely used approach to image processing problems, such as image synthesis, style transfer, and image restoration. At the same time, neural image compression has emerged as a data-driven alternative to traditional coding approaches in visual communications. In this paper, we study the combination of these two paradigms into a joint I2I compression and translation framework, focusing on multi-domain image synthesis. We first propose distributed I2I translation by integrating quantization and entropy coding into an I2I translation framework (i.e. I2Icodec). In practice, the image compression functionality (i.e. autoencoding) is also desirable, requiring to deploy alongside I2Icodec a regular image codec. Thus, we further propose a unified framework that allows both translation and autoencoding capabilities in a single codec. Adaptive residual blocks conditioned on the translation/compression mode provide flexible adaptation to the desired functionality. The experiments show promising results in both I2I translation and image compression using a single model.
Restoration of Single Sand-Dust Image Based on Style Transformation and Unsupervised Adversarial Learning

Since dust particles in the air scatter and absorb light, images captured in sand-dust weather mostly show low contrast, color deviation and blurriness, seriously affecting the reliability of visual tasks. Currently, pixel-level enhancement and prior-based methods are used to restore sand-dust images.However, these methods cannot accurately extract semantic information from the images due to the loss of information and the complexity of the scene depth, which may lead to color distortion and blurred textures of the restored image.We thus presents a two-stage restoration method based on style transformation and unsupervised sand-dust image restoration network(USDR-Net). In the first stage, the grayscale distribution compensation (GDC) method is used to transform the style of the sand-dust image. After transformation, color shift is eliminated and potential information is restored in the balanced image. In the second stage,USDR-Net firstly employs the dark channel prior and the transmission map enhancement network(TME-Network) to generate and refine the transmission map of the balanced image to improve the accuracy of scene depth.Then, it reconstructs a clear image with actual color and high contrast via adversarial learning with unpaired sand-dust and clear images.Extensive experimental results show that our method outperforms state-of-the-art algorithms based on both qualitative and quantitative evaluations.The mean average precision for the target inspection datasets has increased from 16.79% to 68.82%.
Material syntonicity: Examining computational performance and its materiality through weaving and sewing crafts

Background: Fiber crafts occupy a vital position in technology innovation and present a promising space for computer science education, which continues to face lopsided participation.It remains unclear whether and how fiber crafts can become a context for computational learning and what role different materials play with the risk to miss computational approaches that could broaden computational cultures.Methods: Fusing constructionist and posthuman perspectives, this study analyzed how middle school students performed computational concepts while weaving and manipulating fabric and how the craft materials drove what could be learned computationally in these contexts.Findings: Present the fiber crafts as a context for performing computational concepts (i.e., variables, conditionals, functions) and that the materials play a role in what can be learned computationally.While weaving drove computing as the performance of automation, fabric manipulation required speculative and physical three-dimensional modeling as computational.Contribution: The paper presents fiber crafts as a promising context for computational learning and theorizes the ongoing material as material syntonicity, contributing a material direction to fostering more inclusive and sustainable computing cultures.
Smart Quarantine Environment Privacy through IoT Gadgets Using Blockchain

The coronavirus, formerly known as COVID-19, has caused massive global disasters.As a precaution, most governments imposed quarantine periods ranging from months to years and postponed significant financial obligations.Furthermore, governments around the world have used cutting-edge technologies to track citizens' activity.Thousands of sensors were connected to IoT (Internet of Things) devices to monitor the catastrophic eruption with billions of connected devices that use these novel tools and apps, privacy and security issues regarding data transmission and memory space abound.In this study, we suggest a blockchain-based methodology for safeguarding data in the billions of devices and sensors connected over the internet.Various trial secrecy and safety qualities are based on cutting-edge cryptography.To evaluate the proposed model, we recommend using an application of the system, a Raspberry Pi single-board computer in an IoT system, a laptop, a computer, cell phones and the Ethereum smart contract platform.The models ability to ensure safety, effectiveness and a suitable budget is proved by the Gowalla dataset results.
Color image encryption algorithm based on 3D spiral transform and radial diffusion

Abstract While digital image information is convenient to transmit in Internet, there is a great risk of information leakage in practice. To satisfy the demands for image content security and real-time communication, we propose a color image encryption algorithm based on a three-dimensional (3D) spiral transform and radial diffusion. Firstly, a 3D spiral transform is designed by combining the R, G and B channels of the color image with the traditional spiral transform; secondly, after the randomly selecting the spiral starting point by the chaotic sequence, the plained image is scrambled by the 3D spiral transform; thirdly, after randomly selecting the method of radial diffusion by the chaotic sequence, the scrambled image is radially diffused by chaotic sequences to obtain the cipher image. Simulation results and theoretical analyses show that the proposed algorithm not only has an excellent encryption effect, but also has the strong security to resist common attacks. Thus, the proposed algorithm can be suitable for the color image transmission under the network platform with the high-security requirement.
Mapping the spatial distribution of fossil geothermal manifestations and assessment of geothermal potential of the Tangyin rift, Southeast of Taihang Mountain in China

Abstract The spatial prediction of geothermal sites along the southeast side of Taihang Mountain in the Tangyin rift is a critical goal in the development of renewable energy resources in cities such as Hebi in Henan province, which has significant potential for geothermal energy production due to the existence of deep faults and Cenozoic volcano. The study aimed to delineate areas with high geothermal manifestations for detailed geothermal exploration and well-test drilling. To define favorable zones for geothermal potentiality, multi-source datasets were chosen to generate evidence thematic layers that demonstrate the distribution of geothermal anomalies such as geologic maps, structural maps, Land Surface Temperature maps (LST), and hydrothermal mineral alterations maps. The remote sensing and Geographical Information System (GIS) approaches were integrated to map fossil geothermal manifestations and appraise the promising geothermal zones from Landsat 8 OLI/TIRS and Advanced Spaceborne Thermal Emission and Reflection Radiometer-Digital Elevation Model (ASTER-DEM) dataset using the Analytic Hierarchy Process (AHP), which is one of the Mult-criteria Decision Making methods. Results show LST values ranging from 22.6°C to 45.4°C, with the highest values found along the faults and volcanic areas. The hydrothermal alteration minerals discriminated from band ratios of 4/2, 6/5, and 6/7 for iron oxide, ferrous minerals, and clays reveal the presence of high amount of montmorillonite and illite minerals along the faults and volcanoes which are related to hot fluid circulation from subsurface. Structural analysis shows that the major structural trend follows the NNE Tangdong and Tangxi faults, with high density on the west side of the Tangxi fault where there is good exposure of outcrop but less fault density on the east side due to sedimentation in a low land which may conceal geological structures. Lastly, all the thematic layers of geothermal factors were reclassified, ranked, and integrated by the weight overlay logic model based on the influence of geothermal suitability to map potential areas for geothermal resources whereby 5 zones were obtained ranging from very poor to very good. The promising potential sites are mostly found along the highly deep structures of the major Tangxi fault crosscutting the permeable lithology of carbonate rocks and around the Hebi volcanoes. The distribution of hot springs and geothermal wells of the Hebi field were correlated and verified against the potential geothermal anomalies map. The overall integrated method proposes an advanced technique for designing the Tangyin rift’s exploration plan and development actions for geothermal resources utilization, which can be used for exploration of any unveiling geothermal potential sites.
User Experience of the Co-design Research Approach in eHealth: Activity Analysis With the Course-of-Action Framework

The cocreation of eHealth solutions with potential users, or co-design, can help make the solution more acceptable. However, the co-design research approach requires substantial investment, and projects are not always fruitful. Researchers have provided guidelines for the co-design approach, but these are either applicable only in specific situations or not supported by empirical data. Ways to optimize the experience of the co-design process from the point of view of the participants are also missing. Scientific literature in the co-design field generally provides an extrinsic description of the experience of participants in co-design projects.We addressed this issue by describing a co-design project and focusing on the participants' experiences looking at what was significant from their point of view.We used a qualitative situated cognitive anthropology approach for this study. Data were collected on a co-design research project that aimed to support the help-seeking process of caregivers of functionally dependent older adults. The methodology was based on the perspective of experience by Dewey and used the course-of-action theoretical and methodological framework. Data collection was conducted in 2 phases: observation of participants and recording of sessions and participant self-confrontation interviews using the session recordings. We interviewed 27% (20/74) of the participants. We analyzed the data through nonexclusive emerging categorization of themes using the constant comparative method.In total, 5 emerging themes were identified. The perception of extrinsic constraints and the effects of the situation was central and the most important theme, affecting other themes (frustrating interactions with others, learning together, destabilization, and getting personal benefits). Co-occurrences between codes allowed for a visual and narrative understanding of what was significant for the participants during this project. The results highlighted the importance of the role of the research team in preparing and moderating the sessions. They also provided a detailed description of the interactions between participants during the sessions, which is a core aspect of the co-design approach. There were positive and negative aspects of the participants' experiences during this co-design project. Reflecting on our results, we provided potential affordances to shape the experience of participants in co-design.Potential users are an essential component of the co-design research approach. Researchers and designers should seek to offer these users a positive and contributory experience to encourage participation in further co-design initiatives. Future research should explore how the proposed affordances influence the success of the intervention.
Modelling perceived risk in blockchain enabled supply chain utilizing fuzzy-AHP

Purpose Blockchain has been considered a disrupting technology that can add value in various supply chains differently. The provenance framework matches the four blockchain capabilities of traceability, certifiability, trackability and verifiability to the five generic risks, namely, the financial risk, psychological risk, social risk, physical risk and performance risk. This will help in uncording which specific risk gets mitigated by the use of blockchain in a specific supply chain. Design/methodology/approach This study illustrates four supply chains, namely, pharmaceutical industry, fast moving consumer goods industry, precious metal and automotive industry, and maps the risks associated with them to the provenance framework wherein the applicability of blockchain is mapped. Fuzzy analytical hierarchical processing (F-AHP) is used to rank the risks in the supply chain. Findings Blockchain capabilities can elevate the provenance knowledge leading to assurance in terms of origin, authenticity, custody and integrity to mitigate the supply chain risks. Present work highlights the thrust areas across various supply chains and identifies the risk priority tasks aligning the contextual supply chain risks. This study has covered five major risk perceptions. This study contributes to the literature on blockchain, customer perceived risk, provenance and supply chain. Practical implications This methodology can be adopted to understand and market the application of blockchain in a supply chain. It brings the marketers and marketing perspective to the supply chain. Exhaustive risk perception can be included to get more comprehensive data on mapping the risks along different supply chains. Vertical extensions of this work can be consideration of other supply chains including dairy, fruits and vegetables, electronics and component assemblies to derive the comprehensive framework for mapping risk perceptions and thereby supply chain risk mitigation through blockchain technology. Originality/value This linkage between blockchain, perceived risk, applications in the supply chain and a tool to convince the customers about the blockchain applicability has not been discussed in the literature. Adopting the multi-criteria decision-making F-AHP approach, this study attempt to rank the risks and stimulate conversations around a common framework for multiple sectors.
Asymmetric relationships between information and communication technology (ICT), globalization, and human development in India: evidence from non-linear ARDL analysis

Abstract The objective of the paper is to examine the asymmetric relationships between ICT, globalization, and human development in India by analysing the annual data from 1991 to 2019 through the non-linear autoregressive distributed lag (NARDL) model. The result shows that positive (negative) change in globalization leads to a decline (increase) in human development in the long run. Further, a positive change in mobile density increases human development in the long run. A decline in internet density has a negative impact on human development in the long run. In the short run, a positive shock in globalization with one lag has a positive impact on human development. Moreover, a previous year positive shock in internet density has a positive effect on human development while the previous 2 years positive shock in internet density has a negative effect on human development in the short run. A negative shock in internet density with lag one has a negative effect on human development while with lag two it has a positive effect on human development. It is also found that the global financial crisis of 2008 has a negative impact on human development. Thus, it is suggested that India has to promote both globalization and ICT judiciously and consciously in order to improve human development.
On the (Complete) Reasons Behind Decisions

Abstract Recent work has shown that the input-output behavior of some common machine learning classifiers can be captured in symbolic form, allowing one to reason about the behavior of these classifiers using symbolic techniques. This includes explaining decisions, measuring robustness, and proving formal properties of machine learning classifiers by reasoning about the corresponding symbolic classifiers. In this work, we present a theory for unveiling the reasons behind the decisions made by Boolean classifiers and study some of its theoretical and practical implications. At the core of our theory is the notion of a complete reason, which can be viewed as a necessary and sufficient condition for why a decision was made. We show how the complete reason can be used for computing notions such as sufficient reasons (also known as PI-explanations and abductive explanations), how it can be used for determining decision and classifier bias and how it can be used for evaluating counterfactual statements such as “a decision will stick even if ...because ... .” We present a linear-time algorithm for computing the complete reasoning behind a decision, assuming the classifier is represented by a Boolean circuit of appropriate form. We then show how the computed complete reason can be used to answer many queries about a decision in linear or polynomial time. We finally conclude with a case study that illustrates the various notions and techniques we introduced.