Reviewerly/exHarmony huggingface dataset:
An E-resource Sharing Solution Based on Blockchain Technology

By analyzing the problems existing in the sharing platform of e-resource, such as data trust, single point of failure, virtual data property rights disputes, etc, the collection, sorting, display, sharing and other problems of e-resource can be solved with the help of the distributed ledger of blockchain and multi-party consensus, so as to effectively improve the sharing effect of e-resource. The paper analyzes the feasibility of block chain to solve the problem of e-resource sharing through literature research. By drawing on the experience of block chain in the medical field, the paper designs an e-resource sharing scheme based on block chain technology. The scheme is secondary developed on the existing electronic resource sharing platform, and Fabric alliance chain framework and IPFS distributed file storage technology in blockchain are introduced. A four-layer structure model is designed, and the interaction between server, IPFS and Fabric is defined. The semi-structured interview evaluation of participants confirmed that the scheme could improve the availability, reliability and efficiency of the e-resource sharing platform. The proposal can effectively promote the digitalization of modern electronic resources.
Blockchain Privacy Protection Algorithm Based on Pedersen Commitment and Zero-knowledge Proof

Due to the transparency of the blockchain, the data in the blockchain can be viewed by any joining node, and the privacy is weak. In order to better solve the problem of privacy protection in the current blockchain, for the application of e-commerce blockchain, the Pedersen commitment mechanism is adopted to hide the transaction amount, combined with zero-knowledge proof technology to realize the verification of the transaction amount. At the same time, an efficient range proof scheme based on polynomial commitment and vector inner-product commitment is used to verify whether the transaction amount and the balance of both parties are greater than zero. Finally, use the homomorphism promised by Pedersen to update the ciphertext ledger. The security analysis and efficiency test of the proposed blockchain privacy protection algorithm show that compared with the existing schemes, the proposed algorithm has the advantages of strong security and higher efficiency.
Conditional Deep Laplacian Pyramid Image Registration Network in Learn2Reg Challenge

Hyperparameter tuning is extremely tedious and costly in the deep learning-based deformable image registration methods. In this paper, we present our contribution to the Learn2Reg challenge and demonstrate how hyperparameter tuning can be accelerated and simplified with the proposed conditional image registration framework. We exemplify the conditional image registration framework with the deep Laplacian pyramid image registration network (cLapIRN) and apply it comprehensively to all three tasks in the challenge. Our method was ranked the first place in the Learn2Reg 2021 challenge.
Leveraging Blockchain Technology for Internet of Things Powered Banking Sector

Banking sector contributes to 70% of Indian Gross Domestic Product (GDP) and for India to meet its economic aspirations, it should enable this vivacious sector to grow at 8–10 times of its current pace, in the next ten years. This pace of active growth requires a double engine of sophisticated technology and a tech enabled, scalable, and a secured banking system. Implementing BlockchainBlockchain Technology (BCT) in the banking sector, provides a realistic solution which when coupled with devices connected by the Internet of ThingsInternet of Things (IoT), will result in secured, fast-paced, cost effective, and transparent growth of the sector. The prevalence of personalized banking, secured banking, connected bankingConnected banking, and digital banking are use cases, made possible through interface with IoT. This chapter delves into the opportunities in the banking sector to be explored and challenges to be met in the BCT-IoT implementation process. BCT- and IoT-based opportunities such as peer-to-peer lending, Know Your Customer (KYC) updation, Cross-border transfer payments, syndicate lendingSyndicate lending, fraudFraud reduction are some of the banking operations that are elaborated. To strengthen the banking network, the consensus algorithm of BlockchainBlockchain network is much required and the use of IoT devices to act as nodes is pertinent. The blend of both in the banking space has to be further reinforced.Internet of Things
The Opportunities and Challenges for 100% Renewable Energy

A renewable energy revolution is taking place around the world. The growth of renewables is due to the confluence of three key interacting factors: (1) quality and reliability improvements, combined with continuous drops in the capital costs, of renewable technologies and associated balance of systems, which leads to (2) more bankable projects and access to private capital, which further leads to (3) more and more favorable renewable energy policies and targets throughout the world. On top of this, public acceptance of these technologies and a desire for a carbon-free future is further driving the revolution. However, to date, renewable energy (including large-scale hydro) still provides less than one-third of our electrical energy worldwide, although the role of renewables in this particular sector is growing rapidly. And renewables currently play an even smaller role in meeting our much larger heating and transport energy needs (roughly 10% for the heating sector and a little over 3% for the transport sector, according to the 2019 REN21 Global Status Report). Thus, when we target a 100% renewable energy future, our biggest challenge is to transform our entire energy system, not just the power sector, into non-carbon emitting renewables. This paper provides a high-level overview of the current status and trends of renewable energy development (with a focus on the solar sector). This information is presented in the context of some recent key studies that define the extent of the energy transformation that will be required to keep global warming capped at 1.5 °C, as stipulated by the 2015 Paris Climate Agreement and the Intergovernmental Panel on Climate Change (IPCC). The energy transformation analyses that are examined include a recent version of the International Renewable Energy Agency’s (IRENA’s) REMap analysis, the 2019 report on global and regional 100% renewable energy scenario analyses by the University of Sydney, and the 2019 Energy Watch Group/LUT University Report on a 100% Global Renewable Energy System.
A novel optimised method for speckle reduction in medical ultrasound images

The advancement of medical imaging techniques evolving from X-ray to PET images and the medical image analysis helped medical experts to detect, diagnose and offer treatments for complex disorders and deadly diseases in the human body. Among the various modalities used, Ultrasound imaging is the most widely accepted modality because of its affordability, non-invasive nature and various other features. But the presence of speckle noise in ultrasound image lowers the image quality and reduces diagnostic value. This article states an improved hybrid speckle noise reduction method, a combined application of Kuan and non-local means filters. In this method, Kuan filter is used to sharpen the edges and thereafter the speckle noise elimination is done by using the non-local means. In addition, the performance of the proposed hybrid filter and its design parameters are optimised by using a meta-heuristic called grey wolf optimiser. The performance of hybrid method is evaluated by analysing a chosen set of well-known post filtering methods used for speckle reduction with given ultrasound B-mode images. The comparison of test results using remarkable performance metrics and computation time demonstrate that the hybrid method can be used as the efficient speckle reduction method for image analysis.
Preference-Based Assessment of Organisational Risk in Complex Environments

This paper proposes a preference-based approach for optimising the process of organisational risk assessment in complex and uncertain environments, where significant decision-making factors may be interconnected. Organisational risks are herein treated from the perspective of the work-related stress risk involving psycho-physical factors crucial for the safety and well-being of human resources. The traditional Health and Safety Executive (HSE) model commonly used for stress evaluation in working environments is herein improved by first applying the Analytic Network Process (ANP) to weight management standards (MS). This technique has been chosen to avoid neglecting potential relations bounding MS with each other. Finally, Fuzzy Cognitive Maps (FCMs) are used to study dependence among significant stress factors. In such a direction, the support offered by the fuzzy set theory is relevant to deal with subjective evaluations of preference. The case of an Italian airport is analysed to demonstrate the applicability of the approach, and managerial insights are discussed.
Energy saving optimization scheduling of intelligent energy station based on beetle antennae searching-particle swarm optimization algorithm

Abstract New energy and distributed power generation system of smart energy station need to cooperate with energy storage device to carry out peak cutting and valley filling, so as to meet the requirements of grid connection. Rational distribution of power suppression tasks of batteries and other energy storage components will reduce the generation cost of smart energy stations, protect energy storage devices and prolong their life. In this paper, variational modal decomposition algorithm is used to decompose the difference between the original power and the target power and distribute it to each energy storage unit. In addition to considering the decomposition mode number of VMD algorithm, the size of the secondary penalty factor and the power boundary frequency of the energy storage system, the grid-connected target power is also taken as the quantity to be optimized, which further reduces the requirements on the maximum instantaneous power and capacity of the energy storage system. Simulation results show that the proposed method is effective.
Graph Neural Networks Based Multi-granularity Feature Representation Learning for Fine-Grained Visual Categorization

There inherently exists a hierarchy with different levels of classification granularity for object categories. This hierarchy involves rich semantic relationships among categories, which can benefit fine-grained visual categorization (FGVC) but is overlooked by most of previous works. In this paper, a novel graph neural networks based multi-granularity feature representation learning framework is presented for FGVC, which boosts feature learning of different grain levels simultaneously and enhances multiple granularity categorization. Under this framework, we propose two kinds of correlation graphs, i.e., Abstract Graph (AG) and Detailed Graph (DG). AG assigns one node for each grain level while DG regards different categories at each grain level as different nodes. With AG and DG, two graph neural networks based multiple grain feature learning methods are proposed. With AG, graph gate neural network is utilized to explore the interactions between features from different grain levels and help learn more discriminative and comprehensive feature representation for each grain level. Based on DG, we employ graph convolutional network to model the category hierarchical semantic relationships and enhance the feature by regularizing the semantic space division. To facilitate the research, we construct a large-scale car dataset, i.e., Car-FG3K (Available at http://www.nlpr.ia.ac.cn/iva/homepage/jqwang/Car-FG3K.htm ), which covers three-level categories and is more challenging than the existing car datasets in terms of category count and view variation. We conduct experiments on this new dataset and two other datasets, i.e., CUB-200-2011 and FGVC-Aircraft, and our methods achieve comparable results to state-of-the-art methods.
The CADE-28 Automated Theorem Proving System Competition – CASC-28

The CADE ATP System Competition (CASC) is the annual evaluation of fully automatic, classical logic Automated Theorem Proving (ATP) systems. CASC-28 was the twenty-sixth competition in the CASC series. Twenty-two ATP systems competed in the various competition divisions. This paper presents an outline of the competition design and a commentated summary of the results.
Design and experimental demonstration of a hardware-efficient integrated optical neural network

The optical neural network (ONN) is a promising neuromorphic framework for implementing deep learning tasks thanks to the key features of light, such as high parallelism, low latency, and low power consumption. As the size of deep neural networks (DNNs) continues to grow, so do the training and control difficulties of the corresponding photonic hardware accelerators. Therefore, it is essential to reduce the complexity of ONNs while maintaining accuracy. Here we propose an ONN architecture based on structured neural networks to reduce the optical component utilization as well as the chip footprint. The model complexity of our proposed ONN can be further optimized by incorporating current DNN pruning strategies. Meanwhile, a hardware-aware on-chip training flow is also proposed to improve the learnability, trainability, and robustness of our architecture. Finally, we experimentally demonstrate the reliability of this architecture with a programmable photonic neural chip and benchmarked its performance on multiple datasets.
A Greedy Load Balancing Strategy with Optimal Constraints for Edge Computing in Industrial Cloud Environment

Edge computing has gained popularity as the industrial Internet has expanded due to its reduced latency. Some problems, such as task workload management, continue to be troublesome. This article looks at a distributed industrial cloud system with the help of edge computing. The system suggests an alternative static load balancing approach with restrictions to compensate for the drawbacks of dynamic load balancing. It is divided into the following stages to put this plan into action. For the first time, the queue theory is introduced, which predicts how long it will take to finish a task. This article formulates and solves the integer nonlinear programming (NLP) problem, which arises from task load balancing and resource management concerns. The Internet and information technologies have now impacted everything from communication to production. The fast development of current quickly growing technologies such as the big data, industrial Internet, networking, edge computing, cloud computing, and artificial intelligence have aroused the interest of many individuals and organizations.
Language Models Like GPT-3 Could Herald a New Type of Search Engine *

Search engines have become faster and more accurate, even as the web has exploded in size. AI is now used to rank results, and Google uses BERT to understand search queries better. This chapter presents Google, a prototype of a large-scale search engine which makes heavy use of the structure present in hypertext. Google is designed to crawl and index the Web efficiently and produce much more satisfying search results than existing systems.” Search engines are also not good at responding to queries that require answers drawn from multiple sources. Language models today are also skewed toward English, which would leave non-English parts of the web underserved.
Neural network training loss optimization utilizing the sliding innovation filter

Artificial feedforward neural networks (ANN) have been traditionally trained by backpropagation algorithms involving gradient descent algorithms. This is in order to optimize the network’s weights and parameters in the training phase to minimize the out of sample error in the output during testing. However, gradient descent (GD) has been proven to be slow and computationally inefficient in comparison with studies implementing the extended Kalman filter (EKF) and unscented Kalman filter (UKF) as optimizers in ANNs. In this paper, a new method of training ANNs is proposed utilizing the sliding innovation filter (SIF). The SIF by Gadsden et al. has demonstrated to be a more robust predictor-corrector than the Kalman filters, especially in ill-conditioned situations or the presence of modelling uncertainties. In this paper, we propose implementing the SIF as an optimizer for training ANNs. The ANN proposed is trained with the SIF to predict the Mackey-Glass Chaotic series, and results demonstrate that the proposed method results in improved computation time compared to current estimation strategies for training ANNs while achieving results comparable to a UKF-trained neural network.
2.5D Cascaded Semantic Segmentation for Kidney Tumor Cyst

Accurate segmentation of kidney tumours can help doctors diagnose the disease. In this work, we described a multi-stage 2.5D semantic segmentation networks to automatically segment kidney and tumor and cyst in computed tomography (CT) images. First, the kidney is pre-segmented by the first stage network ResSENormUnet; then, the kidney and the tumor and cyst are fine-segmented by the second stage network DenseTransUnet, and finally, a post-processing operation based on a 3D connected region is used for the removal of false-positive segmentation results. We evaluate this approach in the KiTS21 challenge, which shows promising performance.
Intelligent Software Engineering: The Significance of Artificial Intelligence Techniques in Enhancing Software Development Lifecycle Processes

In every sphere of technology nowadays, the world has been moving away from manual procedures towards more intelligent systems that minimize human error and intervention, and software engineering is no exception. This paper is a study on the amalgamation of artificial intelligence with software engineering. Software Development Lifecycle is the foundation of this paper, and each phase of it – Requirements Engineering, Design and Architecture, Development and Implementation, and Testing – serves as a building block. This work elucidates the various techniques of intelligent computing that have been applied to these stages of software engineering, as well as the scope for some of these techniques to solve existing challenges and optimize SDLC processes. This paper demonstrates in-depth, comprehensive research into the current state, advantages, limitations and future scope of artificial intelligence in the domain of software engineering. It is significant for its contributions to the field of intelligent software engineering by providing industry-oriented, practical applications of techniques like natural language processing, meta programming, automated data structuring, self-healing testing etc. This paper expounds upon some open issues and inadequacies of software engineering tools today, and proposes ways in which intelligent applications could present solutions to these challenges.
Toric Surfaces, Linear and Quantum Codes: Secret Sharing and Decoding

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Cognitive System for Traversing the Possible Worlds with Individual Information Processes

In this work, an individual process, or for short, an individual is selected, and the “history” of its transformations is traced, depending on the scenario. Scenario is considered as the restriction imposed on the behavior of individual. States, or separate scenes, are achieved by possible worlds, and transitions between worlds are given by the relation of reachability, or evolvent. The evolvent restricts a transition from one stage of knowledge to later stages. In fact, in this work, the only preliminary work has been done to study the dynamics of the individual and a leading example is formulated. Its formulation gives rise to the induced commutative diagrams in a representative functor category. The mathematical apparatus involved is based on the variable domains and the functor category that represents them. Its purpose is to develop a certain intuition of semantic modeling, and then, if possible, to form a cognitive system. Some limited semantic models have been implemented and are undergoing further practical testing and improvement.
Integrating models and integrated models: towards a unified model of information seeking behaviour

Introduction. There has been a proliferation of information seeking models and theories, each incorporating different viewpoints, concepts, and terminology. While there have been prior attempts at integration, they have all focused on different facets of information seeking behaviour. Method / Analysis. In this paper, I integrate models and frameworks in information seeking behaviour. I considered three groups of models: those that emerged in the 1980s and the 1990s; those from the year 2000 onwards; and integrated models. I mapped the important elements in the chosen models to each other. The unified subset of these elements was then used to create the unified model. Results. I arrive at tables comparing the common elements in different models, a unified model of information seeking behaviour, and examples of propositions derived from the model. The unified model was finally extended to include information behaviour, other than seeking and searching. Conclusions. The unified model should contribute to theory development by drawing attention to the common features and functions of elements in various models. The process of mapping could help researchers synthesise models and create a meta-model, and also serve as a methodological move of taking the work of a theorist and mapping other models to it. The unified model will help system designers view user needs, context, and their information seeking behaviour as central to building context-aware systems.
Denoising Hyperspectral Imageries with Split-Bregman Iteration Scheme

Excluding noise is being imperative before any processing such as any computation or communication from the imageries. This paper demonstrates a denoising scheme for hyperspectral imageries by the usage of an iterative technique of split Bregman methodology. This denoising scheme has been intended for mitigation of mixed noise which includes Gaussian noise along with sparse noise or impulse noise. In this paper, we also attempt to provide a systematic survey and also demonstrate some experimental outcomes which are capable of the minimization of mixed noise from hyperspectral imageries. A split-Bregman methodology iteration approach has shown successful outcomes by quantifying the results in terms of peak signal to noise ratio.
Big Data Energy Internet Artificial Intelligence the Key Technology in the Power Transaction Blockchain

Blockchain, as a rapidly developing technology with features such as distributed, equal security and traceability, is highly compatible with the design idea of the Energy Internet and is expected to become a key technology for the implementation of the Energy Internet. Energy blockchain is a product of the combination of blockchain and the energy industry, which can provide security and value support for all aspects of the energy internet. This paper discusses the current problems in various technical areas and possible future research directions by locating the key technologies in the blockchain of electricity trading in the energy Internet, so as to provide reference for further research and implementation of the energy blockchain.