Reviewerly/exHarmony huggingface dataset:
Blockchain in Insurance: Exploratory Analysis of Prospects and Threats

Ever since the first generation of blockchain technology became very successful and the FinTech enormously benefited from it with the advent of cryptocurrency, the second and third generations championed by Ethereum and Hyperledger have explored the extension of blockchain in other domains like IoT, supply chain management, healthcare, business, privacy, and data management.A field as huge as the insurance industry has been underrepresented in literature.Therefore, this paper presents how investments in blockchain technology can profit the insurance industry.We discuss the basics of blockchain technology, popular platforms in use today, and provide a simple theoretical explanation of the insurance sub-processes which blockchain can mutate positively.We also discuss hurdles to be crossed to fully implement blockchain solutions in the insurance domain.
DDCA: A Distortion Drift-Based Cost Assignment Method for Adaptive Video Steganography in the Transform Domain

Cost assignment plays a key role in coding performance and security of video steganography. Existing cost assignment methods (for adaptive video steganography) are designed for specific transform coefficients rather than all transform coefficients. In addition, existing video steganographic frameworks do not allow Syndrome-Trellis Codes (STCs) to modify all transform coefficients in both intra-coded and inter-coded frames at the same time. To address these limitations, in this article, we first propose a novel video steganographic framework. Then, we give a theoretical analysis of distortion drift in both intra- and inter-coding procedures. Based on the analysis, we design a Distortion Drift-Based Cost Assignment method, hereafter referred to as DDCA. DDCA considers the inner-block, inter-block and inter-frame distortion costs in order to improve the coding performance and the security of stego videos when the embedding payload is fixed. We conducted extensive experiments using two video datasets to evaluate the proposed video steganographic framework and DDCA, in terms of the coding performance and the security. Our experiments show that the proposed framework outperforms three recent state-of-the-art methods, for example the coding performance and the security of stego videos can benefit from DDCA by making full use of all nonzero transform coefficients.
Chaos-Based Secure Communications in Biomedical Information Application

Recently, with the rapid development of biomedical information, establishing secure communication and appropriate security services has become necessary to ensure a secure information exchange process. Therefore, to protect the privacy and confidentiality of personal data, in this study, we use a chaotic system, Lü system of the Lorenz-like system, to generate chaotic signals and apply them to encrypt the biomedical information. In addition, with one of the states of the chaotic system, we design a simple proportional-derivative (PD) controller to synchronize the master-slave chaotic systems for decrypting the biomedical information. Then, we encrypt the biomedical information, electrocardiography (ECG) and electromyography (EMG), measured about 30 s to 60 s to get tens of thousands of data from the subjects at the transmitting side (master) and send them to the receiving side (slave). After the receiving side receives the encrypted information, it decrypts them with the PD controller and then obtains the 1 mV to 2 mV biomedical signals. Thus, the security of the biomedical information can be ensured and realized.
Comparative Analysis of Machine Learning Models for Day-Ahead Photovoltaic Power Production Forecasting

A main challenge for integrating the intermittent photovoltaic (PV) power generation remains the accuracy of day-ahead forecasts and the establishment of robust performing methods. The purpose of this work is to address these technological challenges by evaluating the day-ahead PV production forecasting performance of different machine learning models under different supervised learning regimes and minimal input features. Specifically, the day-ahead forecasting capability of Bayesian neural network (BNN), support vector regression (SVR), and regression tree (RT) models was investigated by employing the same dataset for training and performance verification, thus enabling a valid comparison. The training regime analysis demonstrated that the performance of the investigated models was strongly dependent on the timeframe of the train set, training data sequence, and application of irradiance condition filters. Furthermore, accurate results were obtained utilizing only the measured power output and other calculated parameters for training. Consequently, useful information is provided for establishing a robust day-ahead forecasting methodology that utilizes calculated input parameters and an optimal supervised learning approach. Finally, the obtained results demonstrated that the optimally constructed BNN outperformed all other machine learning models achieving forecasting accuracies lower than 5%.
DeepMerge – II. Building robust deep learning algorithms for merging galaxy identification across domains

ABSTRACT In astronomy, neural networks are often trained on simulation data with the prospect of being used on telescope observations. Unfortunately, training a model on simulation data and then applying it to instrument data leads to a substantial and potentially even detrimental decrease in model accuracy on the new target data set. Simulated and instrument data represent different data domains, and for an algorithm to work in both, domain-invariant learning is necessary. Here, we employ domain adaptation techniques – Maximum Mean Discrepancy as an additional transfer loss and Domain Adversarial Neural Networks – and demonstrate their viability to extract domain-invariant features within the astronomical context of classifying merging and non-merging galaxies. Additionally, we explore the use of Fisher loss and entropy minimization to enforce better in-domain class discriminability. We show that the addition of each domain adaptation technique improves the performance of a classifier when compared to conventional deep learning algorithms. We demonstrate this on two examples: between two Illustris-1 simulated data sets of distant merging galaxies, and between Illustris-1 simulated data of nearby merging galaxies and observed data from the Sloan Digital Sky Survey. The use of domain adaptation techniques in our experiments leads to an increase of target domain classification accuracy of up to ${\sim }20{{\ \rm per\ cent}}$. With further development, these techniques will allow astronomers to successfully implement neural network models trained on simulation data to efficiently detect and study astrophysical objects in current and future large-scale astronomical surveys.
The Current Status of Cryptocurrency Regulation in China and Its Effect around the World

There is no single approach in the world regarding the legal regulation of cryptocurrency. Most countries are wary of legalizing this payment instrument, fearing problems associated with tax evasion, terrorist financing, fraud and other illegal transactions. Nevertheless, the issue of legalization of cryptocurrencies has recently moved to a different level as the market capitalization of cryptocurrencies grew to over USD 237 billion 2020, with several leading cryptocurrencies such as Bitcoin skyrocketing in value in 2021. The explosive growth has been lead in no small part by China, the world’s largest and most important market for cryptocurrency in terms of mining, investing and research. This article reviews the current trends in cryptocurrency regulation with a particular focus on China, including an analysis of current cryptocurrency laws in China, as well as the new Chinese Cryptography Law. Also, it explains recent developments in Chinese regulation and policy will continue to shape the development of the global cryptocurrency markets.
See more than once: Kernel-sharing atrous convolution for semantic segmentation

The state-of-the-art semantic segmentation solutions usually leverage different receptive fields via multiple parallel branches to handle objects of different sizes. However, employing separate kernels for individual branches may degrade the generalization of the network to objects with different scales, and the computational cost increases with the increase of the number of branches. To tackle this problem, we propose a novel network structure, namely Kernel-Sharing Atrous Convolution (KSAC), where branches with different receptive fields share the same kernel, i.e., let a single kernel ‘see’ the input feature maps more than once with different receptive fields. Experiments conducted on the benchmark PASCAL VOC 2012 dataset show that our proposed sharing strategy can not only boost the network’s generalization and representation abilities but also reduce the computational cost significantly. Specifically, on the validation set, when compared with DeepLabv3+, about 2.7G FLOPs and 12.7G FLOPs are saved for output stride = 16 and 8 respectively. In addition, different from the widely used ASPP structure, our proposed KSAC is able to further improve the mIOU by taking benefit of wider context with larger atrous rates. Finally, our KSAC achieves mIOUs of 88.1%, 45.47% and 80.7% on the PASCAL VOC 2012 test set (Everingham et al., 2009), ADE20K dataset (Zhou et al., 2017) and Cityscapes datasets (Marius et al., 2016), respectively. Our full code will be released on Github: https://github.com/edwardyehuang/iSeg.
A Web Interface for Analyzing Hate Speech

Social media services make it possible for an increasing number of people to express their opinion publicly. In this context, large amounts of hateful comments are published daily. The PHARM project aims at monitoring and modeling hate speech against refugees and migrants in Greece, Italy, and Spain. In this direction, a web interface for the creation and the query of a multi-source database containing hate speech-related content is implemented and evaluated. The selected sources include Twitter, YouTube, and Facebook comments and posts, as well as comments and articles from a selected list of websites. The interface allows users to search in the existing database, scrape social media using keywords, annotate records through a dedicated platform and contribute new content to the database. Furthermore, the functionality for hate speech detection and sentiment analysis of texts is provided, making use of novel methods and machine learning models. The interface can be accessed online with a graphical user interface compatible with modern internet browsers. For the evaluation of the interface, a multifactor questionnaire was formulated, targeting to record the users’ opinions about the web interface and the corresponding functionality.
Low-Contact Co-Design: Considering more flexible spatiotemporal models for the co-design workshop

The recent global experience of COVID-19 has problematized the face-to-face co-design process and forced co-design researchers and practitioners to rethink the process of collaboration that typically takes place in a co-design workshop. This paper considers how we might continue to co-design when physical proximity is not possible. Recognising that technology has long played a role in co-design practice, we argue that to date, the technologically mediated experience of co-design has been largely based on the assumption of replicating the physical and embodied experience of the co-design workshop. Rather than accepting the deficit culture implied through the curtailing of much of the conventional face-to-face activities we associate with co-design, this paper reports on proactive research into novel possibilities for continuing collaborative research work through the concept of ‘low-contact co-design’. A series of proprietary visual models that explore a range of spatiotemporal conditions within which co-design practices can occur are presented. Opportunities for engaging with new communities, and in new processes are highlighted and a spatiotemporal framework for planning co-design processes is presented.
Cloud and Cloud Shadow Segmentation for Remote Sensing Imagery Via Filtered Jaccard Loss Function and Parametric Augmentation

Cloud and cloud shadow segmentation are fundamental processes in optical remote sensing image analysis. Current methods for cloud/shadow identification in geospatial imagery are not as accurate as they should, especially in the presence of snow and haze. This article presents a deep learning-based framework for the detection of cloud/shadow in Landsat 8 images. Our method benefits from a convolutional neural network, Cloud-Net+ (a modification of our previously proposed Cloud-Net [1]) that is trained with a novel loss function [filtered Jaccard loss (FJL)]. The proposed loss function is more sensitive to the absence of foreground objects in an image and penalizes/rewards the predicted mask more accurately than other common loss functions. In addition, a sunlight direction-aware data augmentation technique is developed for the task of cloud shadow detection to extend the generalization ability of the proposed model by expanding existing training sets. The combination of Cloud-Net+, FJL function, and the proposed augmentation algorithm delivers superior results on four public cloud/shadow detection datasets. Our experiments on Pascal VOC dataset exemplifies the applicability and quality of our proposed network and loss function in other computer vision applications.
Multi-Level Seg-Unet Model with Global and Patch-Based X-ray Images for Knee Bone Tumor Detection

Tumor classification and segmentation problems have attracted interest in recent years. In contrast to the abundance of studies examining brain, lung, and liver cancers, there has been a lack of studies using deep learning to classify and segment knee bone tumors. In this study, our objective is to assist physicians in radiographic interpretation to detect and classify knee bone regions in terms of whether they are normal, begin-tumor, or malignant-tumor regions. We proposed the Seg-Unet model with global and patched-based approaches to deal with challenges involving the small size, appearance variety, and uncommon nature of bone lesions. Our model contains classification, tumor segmentation, and high-risk region segmentation branches to learn mutual benefits among the global context on the whole image and the local texture at every pixel. The patch-based model improves our performance in malignant-tumor detection. We built the knee bone tumor dataset supported by the physicians of Chonnam National University Hospital (CNUH). Experiments on the dataset demonstrate that our method achieves better performance than other methods with an accuracy of 99.05% for the classification and an average Mean IoU of 84.84% for segmentation. Our results showed a significant contribution to help the physicians in knee bone tumor detection.
The Role of Online Teaching Tools on the Perception of the Students during the Lockdown of Covid-19

Due to COVID-19 pandemic majority of public and private institutions around the world went to lockdown for an unknown end.One of the most important sectors, which faced trouble was education, hence many universities and academic institutions have shifted to online learning via various platforms to make their academic year going.Tishk International University as one of the private universities in Sulaymaniyah has conducted different online platforms to survive academic year.Students' perception toward online education became a great question to be answered during the coronavirus period.Secondary data were collected via questionnaire based on Likert Style, and the results showed that, minor majority of the answers were satisfied in term of experiencing online education, which is considered as accepted because it was first experience.Moreover, even though 40% of them were happy about online education more than half of the respondents received lower than what they have expected.
Making the Last Iterate of SGD Information Theoretically Optimal.

Stochastic gradient descent (SGD) is one of the most widely used algorithms for large-scale optimization problems. While classical theoretical analysis of SGD for convex problems studies (suffix) a...
A detailed review of blockchain-based applications for protection against pandemic like COVID-19

The recent corona virus disease (COVID-19) pandemic has brought the issues of technological deficiencies and challenges of security and privacy, validating and maintaining anonymity, user control over records while fully utilizing the available records etc., that can be encountered in an emergency or pandemic condition. Blockchain technology has evolved as a promising solution in conditions that necessitate immutability, record integrity, and proper records authentication. Blockchain can effectively resolve the technical barriers and effectively utilize the available resources and infrastructure in pandemic situations like the current COVID-19. This paper provides an extensive review of various possible use cases of blockchain and available solutions for protection against the COVID-19 like situation. It gives an insight into the benefits and shortcomings of available solutions. It further provides the issues and challenges of adopting blockchain in a situation like COVID-19 and suggest future directions that can offer a platform for further improved and better solutions.
Implementing COVIDSafe: The Role of Trustworthiness and Information Privacy Law

Governments worldwide view contact tracing as a key tool to mitigate COVID-19 community transmission. Contact tracing investigations are time consuming and labour intensive. Mobile phone location tracking has been a new data-driven option to potentially obviate investigative inefficiencies. However, using mobile phone apps for contact tracing purposes gives rise to complex privacy issues. Governmental presentation and implementation of contact tracing apps, therefore, requires careful and sensitive delivery of a coherent policy position to establish citizen trust, which is an essential component of uptake and use. This article critically examines the Australian Government’s initial implementation of the COVIDSafe app. We outline a series of implementation misalignments that juxtapose an underpinning regulatory rationality predicated on the implementation of information privacy law protections with rhetorical campaigns to reinforce different justifications for the app’s use. We then examine these implementation misalignments from Mayer and colleagues’ lens of trustworthiness (1995) and its three core domains: ability, integrity and benevolence. The three domains are used to examine how the Australian Government’s implementation strategy provided a confused understanding of processes that enhance trustworthiness in the adoption of new technologies. In conclusion, we provide a better understanding about securing trustworthiness in new technologies through the establishment of a value consensus that requires alignment of regulatory rationales and rhetorical campaigning.
An overview of graph databases and their applications in the biomedical domain

Abstract Over the past couple of decades, the explosion of densely interconnected data has stimulated the research, development and adoption of graph database technologies. From early graph models to more recent native graph databases, the landscape of implementations has evolved to cover enterprise-ready requirements. Because of the interconnected nature of its data, the biomedical domain has been one of the early adopters of graph databases, enabling more natural representation models and better data integration workflows, exploration and analysis facilities. In this work, we survey the literature to explore the evolution, performance and how the most recent graph database solutions are applied in the biomedical domain, compiling a great variety of use cases. With this evidence, we conclude that the available graph database management systems are fit to support data-intensive, integrative applications, targeted at both basic research and exploratory tasks closer to the clinic.
Tripartite entropic uncertainty relation under phase decoherence

We formulate the tripartite entropic uncertainty relation and predict its lower bound in a three-qubit Heisenberg XXZ spin chain when measuring an arbitrary pair of incompatible observables on one qubit while the other two are served as quantum memories. Our study reveals that the entanglement between the nearest neighbors plays an important role in reducing the uncertainty in measurement outcomes. In addition we have shown that the Dolatkhah's lower bound (Phys Rev A 102(5):052227, 2020) is tighter than that of Ming (Phys Rev A 102(01):012206, 2020) and their dynamics under phase decoherence depends on the choice of the observable pair. In the absence of phase decoherence, Ming's lower bound is time-invariant regardless the chosen observable pair, while Dolatkhah's lower bound is perfectly identical with the tripartite uncertainty with a specific choice of pair.
Examining the effect of explanation on satisfaction and trust in AI diagnostic systems

Abstract Background Artificial Intelligence has the potential to revolutionize healthcare, and it is increasingly being deployed to support and assist medical diagnosis. One potential application of AI is as the first point of contact for patients, replacing initial diagnoses prior to sending a patient to a specialist, allowing health care professionals to focus on more challenging and critical aspects of treatment. But for AI systems to succeed in this role, it will not be enough for them to merely provide accurate diagnoses and predictions. In addition, it will need to provide explanations (both to physicians and patients) about why the diagnoses are made. Without this, accurate and correct diagnoses and treatments might otherwise be ignored or rejected. Method It is important to evaluate the effectiveness of these explanations and understand the relative effectiveness of different kinds of explanations. In this paper, we examine this problem across two simulation experiments. For the first experiment, we tested a re-diagnosis scenario to understand the effect of local and global explanations. In a second simulation experiment, we implemented different forms of explanation in a similar diagnosis scenario. Results Results show that explanation helps improve satisfaction measures during the critical re-diagnosis period but had little effect before re-diagnosis (when initial treatment was taking place) or after (when an alternate diagnosis resolved the case successfully). Furthermore, initial “global” explanations about the process had no impact on immediate satisfaction but improved later judgments of understanding about the AI. Results of the second experiment show that visual and example-based explanations integrated with rationales had a significantly better impact on patient satisfaction and trust than no explanations, or with text-based rationales alone. As in Experiment 1, these explanations had their effect primarily on immediate measures of satisfaction during the re-diagnosis crisis, with little advantage prior to re-diagnosis or once the diagnosis was successfully resolved. Conclusion These two studies help us to draw several conclusions about how patient-facing explanatory diagnostic systems may succeed or fail. Based on these studies and the review of the literature, we will provide some design recommendations for the explanations offered for AI systems in the healthcare domain.