Reviewerly/exHarmony huggingface dataset:
Overview of Medical Data Privacy Protection based on Blockchain Technology

With the increasing growth of electronic medical data, the difficulties of data sharing among medical institutions and the leakage of data privacy have become the focus of the public and medical workers. The blockchain has the characteristics of decentralization, traceability, and immutability, which can provide new ideas for fine-grained secure access to medical research. This article first introduces blockchain and blockchain-based privacy protection technology; then analyzes the advantages and disadvantages of electronic medical records, and introduces the current development status of electronic medical records based on blockchain technology; then from data encryption, access the three aspects of control and transaction anonymity introduce the medical data privacy protection method based on blockchain technology; finally, the full text is summarized and prospected.
Remote sensing visual question answering with a self-attention multi-modal encoder

Visual Question Answering (VQA) on remote sensing imagery can help non-expert users in extracting information from Earth observation data. Current approaches follow a neural encoder-decoder design, combining convolutional and recurrent encoders together with cross-modal fusion components. However, in other VQA application domains, the current state-of-the-art methods rely on self-attention, employing multi-modal encoders based on the Transformer architecture. In this work, we assess the degree to which a model based on self-attention can bring improvements over previous methods for remote sensing VQA. We specifically present results with an extended version of a previous model named MM-BERT, originally proposed for medical VQA and which does not require the extraction of region features from the images, or model pre-training with extensive amounts of data. Experiments show that the proposed method can improve results over previous approaches. Even without in-domain pre-training or specific adaptations to the remote sensing domain, and using as input low-resolution versions of the images, we can achieve a high accuracy over three different datasets extensively used in previous studies.
Leveraging Smartwatch and Earbuds Gesture Capture to Support Wearable Interaction

Due to the proliferation of smart wearables, it is now the case that designers can explore novel ways that devices can be used in combination by end-users. In this paper, we explore the gestural input enabled by the combination of smart earbuds coupled with a proximal smartwatch. We identify a consensus set of gestures and a taxonomy of the types of gestures participants create through an elicitation study. In a follow-on study conducted on Amazon's Mechanical Turk, we explore the social acceptability of gestures enabled by watch+earbud gesture capture. While elicited gestures continue to be simple, discrete, in-context actions, we find that elicited input is frequently abstract, varies in size and duration, and is split almost equally between on-body, proximal, and more distant actions. Together, our results provide guidelines for on-body, near-ear, and in-air input using earbuds and a smartwatch to support gesture capture.
NFT-based Asset Management System

There are billions of houses, businesses, and lands in the world, and we can prove the ownership of these assets with title deeds prepared by government offices. In the purchase and sale transactions of these titled assets, it is necessary to go through long and complex possess, and the actions that need to be taken do not end here. The asset must also be insured and paid regularly for insurance, tax, and some subscriptions like electricity, water, natural gas, etc. This study aims to create a blockchain-based asset management system that uses NFTs (non-fungible tokens), smart contracts, and the Ethereum network.
Next Word Prediction Using Deep Learning

Next Word Prediction involves guessing the next word which is most likely to come after the current word. The system suggests a few words. A user can choose a word according to their choice from a list of suggested word by system. It increases typing speed and reduces keystrokes of the user. It is also useful for disabled people to enter text slowly and for those who are not good with spellings. Previous studies focused on prediction of the next word in different languages. Some of them are Bangla, Assamese, Ukraine, Kurdish, English, and Hindi. According to Census 2011, 43.63% of the Indian population uses Hindi, the national language of India. In this work, deep learning techniques are proposed to predict the next word in Hindi language. The paper uses Long Short Term Memory and Bidirectional Long Short Term Memory as the base neural network architecture. The model proposed in this work outperformed the existing approaches and achieved the best accuracy among neural network based approaches on IITB English-Hindi parallel corpus.
Uniformity improvement of Josephson-junction resistance by considering sidewall deposition during shadow evaporation for large-scale integration of qubits

Abstract The uniformity of Josephson-junction (JJ) characteristics is crucial in wafer-scale superconducting quantum-bit (qubit) integration. To achieve the level of accuracy demanded the circuits, every detail of the fabrication process needs to be optimized. Here we reveal that the junction-resistance ( R N ) variations of Al/AlO x /Al JJs are affected by the metal deposition on the sidewall of the resist mask during shadow evaporation. The effect is reproduced in numerical simulation using a simple model taking into account the resist structure and the evaporation angle. To overcome the issue, we introduce a two-step shadow evaporation method to reduce the variation of R N . As a result, the coefficient of variations across a 3-inch wafer decreases from 6.7% to 4.5%, achieving 1.1% in a chip with an area of 10 mm × 10 mm. This method is promising for developing large-scale superconducting quantum computers.
Deep Learning Implemented Visualizing City Cleanliness Level by Garbage Detection

In an urban city, the daily challenges of managing cleanliness are the primary aspect of routine life, which requires a large number of resources, the manual process of labour, and budget.Street cleaning techniques include street sweepers going away to different metropolitan areas, manually verifying if the street required cleaning taking action.This research presents novel street garbage recognizing robotic navigation techniques by detecting the city's street-level images and multi-level segmentation.For the large volume of the process, the deep learning-based methods can be better to achieve a high level of classification, object detection, and accuracy than other learning algorithms.The proposed Histogram of Oriented Gradients (HOG) is used to features extracted while using the deep learning technique to classify the ground-level segmentation process's images.In this paper, we use mobile edge computing to process street images in advance and filter out pictures that meet our needs, which significantly affect recognition efficiency.To measure the urban streets' cleanliness, our street cleanliness assessment approach provides a multi-level assessment model across different layers.Besides, with ground-level segmentation using a deep neural network, a novel navigation strategy is proposed for robotic classification.Single Shot Mul-tiBox Detector (SSD) approaches the output space of bounding boxes into a set of default boxes over different feature ratios and scales per attribute map location from the dataset.The SSD can classify and detect the garbage's accurately and autonomously by using deep learning for garbage recognition.Experimental results show that accurate street garbage detection and navigation can reach approximately the same cleaning effectiveness as traditional methods.
Metric entropy and digital image correlation in deformation dynamics analysis of fibre glass reinforced composite under uniaxial tension

This paper proposes a combined method for the mechanical properties assessment of composite materials based on the analysis of the internal dynamics of raw data from uniaxial tensile tests. Determination of the Kolmogorov-Sinai (K-S) metric entropy from the recorded data enables to find the tensile strength of materials. The correctness of the methodology proposed was verified by the full filed DIC measurements. Therefore, the main aim of this research was to apply independent measurement tools in the analysis of the non-linear dynamics supported by their mutual validation. The experimental program carried out on the glass fibre-based thermoplastic Elium acrylic composite and E752 Carbon Fiber Reinforced Epoxy (CFRE) composite confirmed the high suitability of the presented methodology in ultimate tensile stress identification, and thus, its great applicability potential in many aspects of the contemporary engineering.
Supply chain transparency as a signal of ethical production

Abstract We analyze firms' supply chain transparency and ethical production practices using a game‐theoretic approach. If the rival's supply chain is more likely to be ethical and the technology used to ensure supply chain transparency is efficient, ethical suppliers prefer to disclose their supply chain information, whereas unethical suppliers do not. The supply chain transparency level can be a signal of the supplier's ethical quality level, as both consumers' demand for ethical production and R&amp;D efficiency for supply chain transparency increase. Consumers can distinguish ethical suppliers through their level of supply chain transparency because blockchain technology improves R&amp;D efficiency.
Solar photovoltaic power forecasting for microgrid energy management system using an ensemble forecasting strategy

Solar photovoltaic (PV) power production is always unpredictable due to intermittent solar irradiance and weather parameters which pose a major challenge in microgrid energy management systems (M-EMS). An accurate solar PV power forecasting strategy is therefore important for the competent M-EMS that can lead to financial gains for customers and the power industry. In this paper, an ensemble forecasting strategy has been proposed to predict the solar PV power for M-EMS based on the combination of four different forecasting methods, i.e. tunicate swarm algorithm (TSA)-based least-square support vector machine (LSSVM), TSA-based multilayer perceptron neural network (TSA-MLPNN), whales optimization algorithm (WOA)-based LSSVM, and WOA-based MLPNN (WOA-MLPNN). The output of each forecasting model is aggregated through the Bayesian model averaging (BMA) method. The historical data of solar PV power and influencing factors obtained from the N-78 Griffith University Nathan campus have been employed to analyze and verify the performance of the proposed strategy. The simulation is carried out using MATLAB-2018 and the comparison of proposed ensemble strategy has been drawn with several competitive strategies. The simulation results indicate that the proposed forecasting strategy minimizes the root means square error by 29%, 33%, 27%, 33%, 58%, 12%, 26% and 43% in comparison with TSA-MLPNN, TSA-LSSVM, particle swarm optimization (PSO)-based artificial neural network (ANN), PSO-LSSVM, backpropagation neural network, ensemble strategy-1, ensemble strategy-2, and ensemble strategy-3, respectively.
Pre-established entanglement distribution algorithm in quantum networks

As the basic principle of quantum networks, quantum entanglement can enable important quantum applications such as teleportation and quantum-key distribution. To realize wide-area quantum communication, entanglement establishment between two remote communication parties is vital, and it requires effective entanglement distribution strategies. Entanglement distribution network models of distributed and centralized entangled pair sources are constructed, based on which two entanglement-distribution algorithms are proposed, i.e., the real-time entanglement distribution (R-TED) algorithm and the pre-established entanglement distribution (P-EED) algorithm, to achieve end-to-end multi-hop entanglement establishment. For the former, the objective is to build long-distance entanglements via hop-by-hop entanglement tentatively and entanglement swapping to finally glue them together. For the latter, which uses pre-established entanglement, entanglements can be established in advance to patch multiple link-level entanglements via entanglement swapping. Simulation results show that as the number of time slots increases, the P-EED algorithm is more efficient and has higher entanglement establishment probability than the R-TED algorithm to establish end-to-end entanglement; while there are fewer memory cells in a quantum memory, such as 10, the R-TED algorithm provides more stable entanglement distribution compared to the P-EED algorithm.
Entrepreneurial Opportunities in Metaverse

The Metaverse offers an evolving landscape for extending the virtual, augmented, and extended reality on one hand, and overlapping such technologies with the real world on the other hand. Metaverse brings an immersive internet space at the tipping point of transforming the internet and influences the interaction of virtual and real worlds. This transformative impact will be exhibited across different dimensions of business and offer a wide range of entrepreneurial opportunities for entrepreneurs. This chapter discusses the entrepreneurial opportunities offered by metaverse across different sectors like digital marketing, advertisements, games, education, tourism, etc., which would reduce social inequalities and transform business.
Robotic surgery remote mentoring via AR with 3D scene streaming and hand interaction

With growing popularity of robotic surgery, education becomes increasingly important and urgently needed. However, experienced surgeons have limited accessibility due to busy clinical schedules or working in a distant city, thus can hardly provide sufficient education resources for novices. Remote mentoring, as an effective way, can solve this problem, but traditional methods are limited to plain text, audio, or 2D video, which are not intuitive nor vivid. Augmented reality (AR) offers new possibilities for interactive teaching. In this paper, we propose a novel AR-based robotic surgery remote mentoring system with efficient 3D scene visualisation and natural hand interaction. Using a head-mounted display, mentors can remotely monitor the procedure streamed from trainees’ operation side. Mentors can also provide feedback directly with hand gestures, which is transmitted to trainees and viewed in robot console as guidance. We comprehensively validate the system on both real surgery videos and ex-vivo training tasks (peg-transfer and suturing). Promising results are demonstrated regarding fidelity of streamed scene visualisation, accuracy of feedback with hand interaction, and low-latency of each component in the system. This work showcases the feasibility of leveraging AR for reliable, flexible and low-cost solutions to robotic surgical education, and holds great potential for clinical applications.
Firm diversity and data breach risk: A longitudinal study

• There exists a significant negative relationship between firm diversity and data breach risks. • The relationship between firm diversity and data breach risks is more evident under related than unrelated diversity. • Managerial ability strengthens the negative relationship between firm diversity and data breach risks. Research has extensively investigated the rationale of firm diversity from the economic perspective, but little is known about how such a strategy may affect information security. The present study is the first to examine how firm diversity is relevant to firms’ likelihood to experience data breaches (i.e., data breach risk). Drawing from the strands of literature on information security, diversification, and resource-based view, we propose hypotheses on the relationship between firm diversity and data breach risk, as well as the boundary conditions of this relationship. On the basis of a twelve-year sample of publicly-listed firms, our analysis provides evidence to support the negative association between firm diversity and data breach risk. Our analysis also delineates conditions under which the effects of firm diversity can intervene to reduce the data breach risk invoked, such as under related diversity and when managers are managerially capable. For academics, our research accentuates an intriguing but unexamined benefit of firm diversity because it relates to information security. For practicing professionals, this research highlights the significant impact of firms’ operational structure on information security.
Compact Image-Style Transfer: Channel Pruning on the Single Training of a Network

Recent image-style transfer methods use the structure of a VGG feature network to encode and decode the feature map of the image. Since the network is designed for the general image-classification task, it has a number of channels and, accordingly, requires a huge amount of memory and high computational power, which is not mandatory for such a relatively simple task as image-style transfer. In this paper, we propose a new technique to size down the previously used style transfer network for eliminating the redundancy of the VGG feature network in memory consumption and computational cost. Our method automatically finds a number of consistently inactive convolution channels during the network training phase by using two new losses, i.e., channel loss and xor loss. The former maximizes the number of inactive channels and the latter fixes the positions of these inactive channels to be the same for the image. Our method improves the image generation speed to be up to 49% faster and reduces the number of parameters by 20% while maintaining style transferring performance. Additionally, our losses are also effective in pruning the VGG16 classifier network, i.e., parameter reduction by 26% and top-1 accuracy improvement by 0.16% on CIFAR-10.
A multi-tasking model of speaker-keyword classification for keeping human in the loop of drone-assisted inspection

Audio commands are a preferred communication medium to keep inspectors in the loop of civil infrastructure inspection performed by a semi-autonomous drone. To understand job-specific commands from a group of heterogeneous and dynamic inspectors, a model must be developed cost-effectively for the group and easily adapted when the group changes. This paper is motivated to build a multi-tasking deep learning model that possesses a Share–Split–Collaborate architecture. This architecture allows the two classification tasks to share the feature extractor and then split subject-specific and keyword-specific features intertwined in the extracted features through feature projection and collaborative training. A base model for a group of five authorized subjects is trained and tested on the inspection keyword dataset collected by this study. The model achieved a 95.3% or higher mean accuracy in classifying the keywords of any authorized inspectors. Its mean accuracy in speaker classification is 99.2%. Due to the richer keyword representations that the model learns from the pooled training data Adapting the base model to a new inspector requires only a little training data from that inspector Like five utterances per keyword. Using the speaker classification scores for inspector verification can achieve a success rate of at least 93.9% in verifying authorized inspectors and 76.1% in detecting unauthorized ones. Further The paper demonstrates the applicability of the proposed model to larger-size groups on a public dataset. This paper provides a solution to addressing challenges facing AI-assisted human–robot interaction Including worker heterogeneity Worker dynamics And job heterogeneity.
A Design of Resource Allocation Structure for Multi-Tenant Services in Kubernetes Cluster

Nowadays, for multi-tenant cloud, there are plenty of Kubernetes-based open-source projects that do extraordinary in management, isolation multitenancy. These systems allow multiple customers to share the same clusters by dividing them into multiple logical clusters. To satisfy each tenant different workload demand, resource allocation is handled manually by the multi-tenant system administrator. However, this static resource provisioning method cannot adapt to address workload bursting or scaling enough Pod to serve at peak hour and scaling down the number of Pods at least hour. In our paper, we propose a design of dynamic resource allocation in Kubernetes multi-tenancy system to address the missing dynamic resource allocation in the multi-tenant Kubernetes control plane. We start with a controller interacting with the multi-tenant system, which offers dynamic resource allocation, automated scheduling workloads, and policies-based placement. We also present the preliminary results demonstrating the applicability of allocating resources to tenants' applications and services to adapt to workloads changes. Our initial results show fast Pod creation time with the help of a policies-based scheduler and a resources allocator to tenant's applications.
Detecting fake-review buyers using network structure: Direct evidence from Amazon

Online reviews significantly impact consumers' decision-making process and firms' economic outcomes and are widely seen as crucial to the success of online markets. Firms, therefore, have a strong incentive to manipulate ratings using fake reviews. This presents a problem that academic researchers have tried to solve for over two decades and on which platforms expend a large amount of resources. Nevertheless, the prevalence of fake reviews is arguably higher than ever. To combat this, we collect a dataset of reviews for thousands of Amazon products and develop a general and highly accurate method for detecting fake reviews. A unique difference between previous datasets and ours is that we directly observe which sellers buy fake reviews. Thus, while prior research has trained models using laboratory-generated reviews or proxies for fake reviews, we are able to train a model using actual fake reviews. We show that products that buy fake reviews are highly clustered in the product reviewer network. Therefore, features constructed from this network are highly predictive of which products buy fake reviews. We show that our network-based approach is also successful at detecting fake review buyers even without ground truth data, as unsupervised clustering methods can accurately identify fake review buyers by identifying clusters of products that are closely connected in the network. While text or metadata can be manipulated to evade detection, network-based features are more costly to manipulate because these features result directly from the inherent limitations of buying reviews from online review marketplaces, making our detection approach more robust to manipulation.
Supervised Object-Specific Distance Estimation from Monocular Images for Autonomous Driving

Accurate distance estimation is a requirement for advanced driver assistance systems (ADAS) to provide drivers with safety-related functions such as adaptive cruise control and collision avoidance. Radars and lidars can be used for providing distance information; however, they are either expensive or provide poor object information compared to image sensors. In this study, we propose a lightweight convolutional deep learning model that can extract object-specific distance information from monocular images. We explore a variety of training and five structural settings of the model and conduct various tests on the KITTI dataset for evaluating seven different road agents, namely, person, bicycle, car, motorcycle, bus, train, and truck. Additionally, in all experiments, a comparison with the Monodepth2 model is carried out. Experimental results show that the proposed model outperforms Monodepth2 by 15% in terms of the average weighted mean absolute error (MAE).