Reviewerly/exHarmony huggingface dataset:
Therapeutic Effect of Electronic Endoscopic Hematoma Removal on Hypertensive Basal Ganglia Cerebral Hemorrhage Based on Smart Medical Technology

The medical and health industry has successively experienced three stages of digital medical treatment, local area network medical treatment, and internet medical treatment. With the rapid development of technologies such as the Internet of Things, big data, and artificial intelligence, emerging applications and service models have gradually penetrated into all aspects of the medical and health field. At this point, the informatization development process of the medical industry has entered the stage of smart medical treatment. (Smart medical system is a new medical system that improves users' medical experience and provides users with better services. Due to the cumbersome, complicated, and mechanically rigid environment of the past medical service, there was no uniform standard. In order to create a reliable and open medical service environment, an intelligent medical system came into being.). A diversified technical foundation and smart medical protection, conducive to providing patients with high-quality medical services, are established. This article mainly introduces the analysis of the therapeutic effect of smart medical electronic endoscopic hematoma removal on hypertensive basal ganglia cerebral hemorrhage and aims to inject advanced technology and vitality of smart medical treatment into the treatment of hypertensive basal ganglia cerebral hemorrhage by hematoma removal and help the doctor to treat the patient. This article proposes the research methods of smart medical application in the treatment of hypertensive basal ganglia cerebral hemorrhage with electronic endoscopic hematoma removal, including smart medical overview, intracranial hematoma removal for hypertensive basal ganglia cerebral hemorrhage, and smart medical bioelectric signal classification. The recognition algorithm is used to realize the smart medical application of the electronic endoscopic hematoma removal in the treatment of hypertensive cerebral hemorrhage in the basal ganglia area. The experimental results show that the removal of intracranial hematoma based on smart medicine can effectively improve the removal rate of intracranial hematoma, with a recovery rate of 26.73% and a significant efficiency of 36.49%.
EduChain: A highly available education consortium blockchain platform based on Hyperledger Fabric

Summary With the problems of data sharing and information diddling in the field of education, we construct a highly available education consortium blockchain platform to ensure trusted sharing and privacy protection of education data. We employ erasure codes to process blockchain ledger files and optimize the data storage model according to the characteristics of education data, which can reduce the storage volume effectively. A HotStuff consensus algorithm is designed to access the ordering service of Hyperledger Fabric. A suitable educational blockchain network architecture based on the node complexity of education scenarios is proposed to achieve the high availability of the platform. To manage the education blockchain network, we implement the Fabric deployment based on Kubernetes and achieve the goal of including chaincode into Kubernetes environmental management. To improve the resource utilization of chaincode, we explore the new way of chaincode management by the functional computing service. Finally, on the premise of ensuring a 1/2 fault tolerance rate, the total ledger has decreased by 53.56%. Our platform enhanced the Byzantine fault tolerance while ensuring higher efficiency. Experimental results show that our platform is quite suitable for education scenario with many nodes.
Delay optimization for ternary fixed polarity Reed–Muller circuits based on multilevel adaptive quantum genetic algorithm

Delay optimization has now emerged as an important optimization goal in logic synthesis. The delay optimization for ternary fixed polarity Reed–Muller (FPRM) circuits aims to find a ternary FPRM circuit with a minimum delay. Because the delay optimization for ternary FPRM circuits is a combinatorial optimization problem, in this paper, we first propose a multilevel adaptive quantum genetic algorithm (MAQGA), which divides individuals into three-level populations: high-level population, intermediate-level population, and low-level population and uses the proposed ternary quantum rotation gate, proposed ternary quantum correction gate, and proposed multi-operator adaptive mutation mechanism to make the three-level populations evolve. Moreover, based on the proposed delay decomposition strategy, we propose a delay optimization approach (DOA) for ternary FPRM circuits under the unit delay model, which searches for a ternary FPRM circuit with a minimum delay using the MAQGA. Experimental results demonstrated the effectiveness and superiority of the DOA in optimizing the delay of ternary FPRM circuits.
Multi-Level Temporal Dilated Dense Prediction for Action Recognition

3D convolutional neural networks have achieved great success for action recognition. However, large variations of temporal dynamics have not been properly processed and low-level features have not been fully exploited in most existing works. To solve these two problems, we present a general and flexible framework, namely multi-level temporal dilated dense prediction network, which can incorporate with most of existing methods as backbone to improve the temporal modeling capacity. In the proposed method, a novel temporal dilated dense prediction block is designed to fully utilize temporal features with various temporal dilated rates for dense prediction while maintaining relatively low computational cost. To fuse information from low to high levels, our method combines the predictions from multiple such blocks inserted at different stages of the backbone network. In-depth analysis is given to show that short- to long-term temporal dependencies can be captured and multi-level spatio-temporal features are effectively fused for video action recognition by the proposed method. Experimental results demonstrate that our method achieves impressive performance improvement on four publicly available action recognition benchmarks including Charades, Kinetics, Something-Something-V1 and HMDB51.
Research on the Application of Computer Intelligent Proofreading System in College English Teaching

Abstract With the rapid development of computer technology, people have higher requirements for English Teaching (hereinafter referred to as ET) tools. However, in ET, teachers need to correct students’ homework, which will take a lot of time. Therefore, we can develop intelligent proofreading system (hereinafter referred to as IPS), which can improve the translation and proofreading of English syntax and phrases. Through corpus and other databases, we can calibrate English phrases and syntax with high accuracy, which will improve phrase translation and poor sentence coherence. By creating semantic ontology model and phrase translation combination translation algorithm, we can create semantic ontology model, which will better realize online collaborative translation and intelligent school peer-to-peer work, which will play an important role in ET. Through the IPS, we can display the situation in a graphical way, which will better carry out teaching activities. Firstly, this paper analyzes the positioning of IPS for College ET. Then, this paper analyzes the working principle of the IPS. Finally, some suggestions are put forward.
Web Scraping Using Summarization and Named Entity Recognition (NER)

“In the age of information, ignorance is a choice” Donny Miller, this line perfectly encapsulates the relevance and importance of information in the present digital era. With the rise and spread of the internet, growth and prevalence of social media usage in the youth demographic and tons of data being generated by different businesses and industries, there has been an exponential rise in information being generated on a daily basis on multiple platforms and across all demographics of modern society. This significant rise in information generation has made the development of information processing and analyzing techniques imperative. The spread of the internet across the globe, has made it the largest repository of information and data. Internet companies, stock companies, market analyzers and various other businesses use sophisticated tools and techniques to extract information from the internet. One of the most important and prevalent method of extracting relevant data off the world-wide web is Web Scraping. Web scraping has gained significant popularity due to the ease it offers in extracting information from target webpages and presenting the information in a structured format with no manual intervention. While the traditional approach to web scraping offers significant advantages, it also necessitates foreknowledge of the DOM structure of the target webpages. In the subsequent sections of this publication, an excellent method that allows developers to bypass the aforementioned requirement, and completely automates the process of web crawling and web scraping relevant information from target URLs is presented. In this paper Natural Language Processing (NLP) and Machine Learning (ML) alternatives to the traditional web-scraping approach is presented. To demonstrate the advantages offered by the improved algorithm, an epidemic predictor mapping the spread of a variety of infectious/viral diseases and their impact across the globe is built using the alternative methods provided in the publication.
CEBRA: A CasE-Based Reasoning Application to recommend banking products

Following data ethics and respecting the clients’ privacy, the banking environment can use the client data that is available to them to offer personalized services to its clients. Intelligent recommender systems can support this attempt through specialized technological architectures. This article proposes the inclusion of CEBRA (CasE-Based Reasoning Application), a case-based reasoning system oriented to commercial banking, in a Fog Computing architecture coordinated by virtual agents. Throughout this article, the model of this architecture is presented and its life cycle is described, and improvements are proposed through the incorporation of several techniques in the retrieve and reuse phases, including the extraction of interests expressed by users on their social network profiles and collaborative filtering systems. A comprehensive case study has been carried out and a dataset of 60,000 cases has been generated to evaluate CEBRA. As a result, the Recommender System is presented, by including, the recommendation algorithm and a REST interface for its use. The recommendations are based on the user’s profile, previous ratings and/or additional knowledge such as the user’s contextual information. The proposal takes advantage of contextual information to support the promotion of banking and financial products, improving user satisfaction.
An Improved Data Hiding using Pixel Value Difference Method and Hyperchaotic System

Abstract The present paper builds a security system to encrypt and hide important text data. The system utilized an AES method to conduct an encryption process, followed by hiding the encrypted data using an improved Pixel Value Difference (PVD) technique. The method works to builds a map to hide data in a non-sequential way by relying on a hyperchaotic system to increase the security level. The system methodology proposed that the data embedding process is in one of the three levels of the colour image (Red, Green, and Blue), where the embedding level will determined based on the coordinates of the PVD pair points that is increase the efficiency of performance. A set of measures was used to measure the quality of steganography where we used MSE, PSNR, SNR, and Corr, and the results are well and satisfactory. The proposed method records the least MSE value with 0.85348784908256, while the Corr values did not decrease about 0.994145776227782. The proposed method also proved successful and effective in retrieving and decoding data, where the BER scale was equal to zero for all retrieved text.
Blockchain based Voting system in Local Network

Structuring an electronic voting system which fulfills the legitimate requirements of representatives has been a challenge for a long time. Conducting the free, systematic and impartial election is the vital goal of every democracy nation. Every country follows a different voting system from old paper ballot system to Electronic voting system. There facing a many problem in these voting systems. The main problem is location and the accessibility, people are suffering to go to their native place polling booth for casting their vote. This needs to be considered as every people`s vote plays significant role in deciding the right leaders. Blockchain technology offers the transparency and security requisites for the impartial election. It is a complete decentralized, immutable ledger system. The online voting system allows the voters to cast their vote from any place at any time which leads to increasing the voter participation count. The objective of the paper is to create a voting system which provides transparency and security using Blockchain technology, the Ganache tool is used for setting up a local blockchain network. The metamask is used for account verification.
Broad Colorization

The scribble- and example-based colorization methods have fastidious requirements for users, and the training process of deep neural networks for colorization is quite time-consuming. We instead proposed an automatic colorization approach with no dependence on user input and no need to endure long training time, which combines local features and global features of the input gray-scale images. Low-, mid-, and high-level features are united as local features representing cues existed in the gray-scale image. The global feature is regarded as data prior to guiding the colorization process. The local broad learning system is trained for getting the chrominance value of each pixel from the local features, which could be expressed as a chrominance map according to the position of pixels. Then, the global broad learning system is trained to refine the chrominance map. There are no requirements for users in our approach, and the training time of our framework is an order of magnitude faster than the traditional methods based on deep neural networks. To increase the user's subjective initiative, our system allows users to increase training data without retraining the system. Substantial experimental results have shown that our approach outperforms state-of-the-art methods.
Building and Designing Expressive Speech Synthesis

chapter Building and Designing Expressive Speech Synthesis Share on Authors: Matthew P. Aylett View Profile , Leigh Clark View Profile , Benjamin R. Cowan View Profile , Ilaria Torre View Profile Authors Info & Claims The Handbook on Socially Interactive Agents: 20 years of Research on Embodied Conversational Agents, Intelligent Virtual Agents, and Social Robotics Volume 1: Methods, Behavior, CognitionSeptember 2021 Pages 173–212https://doi.org/10.1145/3477322.3477329Online:02 October 2021Publication History 0citation3DownloadsMetricsTotal Citations0Total Downloads3Last 12 Months3Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
SecRec: A Privacy-Preserving Method for the Context-Aware Recommendation System

Context-aware recommendation systems are of increasing popularity in the digital era to recommend personalized items to users. However, how to ensure user data privacy while remaining high recommendation accuracy is widely considered a challenge. In this work, we propose a privacy-preserving method for the context-aware recommendation system in the two-cloud model. In particular, we first adjust the standard additive secret sharing scheme to support secure negative integers computation, based on which we manage to design secure comparison protocol and division protocols that enjoy desirable security and efficiency. By using these new protocols, we propose a secure and efficient context-aware recommendation system that also supports offline users. Compared with the state-of-the-art, our scheme achieves stronger data privacy preservation by further protecting the intermediate data calculated during the system training. Experimental results on real-world datasets indicate that our scheme is efficient. Notable, our system could achieve more significant performance improvement by running the underlying schemes in parallel.
Wave-optics simulation of dynamic speckle: I. In a pupil plane

This two-part paper demonstrates the use of wave-optics simulations to model the effects of dynamic speckle. In Part I, we formulate closed-form expressions for the analytical irradiance correlation coefficient, specifically in the pupil plane of an optical system. These expressions are for square, circular, and Gaussian scattering spots and four different modes of extended-object motion, including in-plane and out-of-plane translation and rotation. Using a phase-screen approach, we then simulate the equivalent scattering from an optically rough extended object, where we assume that the surface heights are uniformly distributed and delta correlated from grid point to grid point. For comparison to the analytical irradiance correlation coefficient, we also calculate the numerical irradiance correlation coefficient from the dynamic speckle after propagation from the simulated object plane to the simulated pupil plane. Overall, the analytical and numerical results definitely demonstrate that, relative to theory, the dynamic speckle in the simulated pupil plane is properly correlated from one frame to the next. Such validated wave-optics simulations provide the framework needed to model more sophisticated setups and obtain accurate results for system-level studies.
IFFO: An Improved Fruit Fly Optimization Algorithm for Multiple Workflow Scheduling Minimizing Cost and Makespan in Cloud Computing Environments

Cloud computing platforms have been extensively using scientific workflows to execute large-scale applications. However, multiobjective workflow scheduling with scientific standards to optimize QoS parameters is a challenging task. Various metaheuristic scheduling techniques have been proposed to satisfy the QoS parameters like makespan, cost, and resource utilization. Still, traditional metaheuristic approaches are incompetent to maintain agreeable equilibrium between exploration and exploitation of the search space because of their limitations like getting trapped in local optimum value at later evolution stages and higher-dimensional nonlinear optimization problem. This paper proposes an improved Fruit Fly Optimization (IFFO) algorithm to minimize makespan and cost for scheduling multiple workflows in the cloud computing environment. The proposed algorithm is evaluated using CloudSim for scheduling multiple workflows. The comparative results depict that the proposed algorithm IFFO outperforms FFO, PSO, and GA.
Exploring cost-efficient bundling in a multi-cloud environment

When exploring hybrid cloud solutions, the provision of cloud computing resources by more than one provider, may result in important gains, such as better risk management, improvement of operational efficiency and avoidance of the lock-in effect by a single vendor. To this end, many enterprises decide to rely on hybrid strategies and multiple cloud providers. With the strategic distribution of the cloud management and resources between multiple providers, multi-cloud adoption can achieve greater efficiency, taking advantage of the economies of scale, and establish an optimal performance/cost balance of the multi-cloud service. Into this context, the paper explores the pricing strategies of 23 different providers for IaaS and estimates the efficiency of different multi-provider service bundles, utilizing DEA methodology. The results are quite encouraging, since the efficiency of multi-cloud solutions is increasing, while cost savings may be accomplished. They provide a measurable driver for clients exploring hybrid solutions of how to mix different IaaS services of different providers.
FIVES: Feature Interaction Via Edge Search for Large-Scale Tabular Data

High-order interactive features capture the correlation between different columns and thus are promising to enhance various learning tasks on ubiquitous tabular data. To automate the generation of interactive features, existing works either explicitly traverse the feature space or implicitly express the interactions via intermediate activations of some designed models. These two kinds of methods show that there is essentially a trade-off between feature interpretability and search efficiency. To possess both of their merits, we propose a novel method named Feature Interaction Via Edge Search (FIVES), which formulates the task of interactive feature generation as searching for edges on the defined feature graph. Specifically, we first present our theoretical evidence that motivates us to search for useful interactive features with increasing order. Then we instantiate this search strategy by optimizing both a dedicated graph neural network (GNN) and the adjacency tensor associated with the defined feature graph. In this way, the proposed FIVES method simplifies the time-consuming traversal as a typical training course of GNN and enables explicit feature generation according to the learned adjacency tensor. Experimental results on both benchmark and real-world datasets show the advantages of FIVES over several state-of-the-art methods. Moreover, the interactive features identified by FIVES are deployed on the recommender system of Taobao, a worldwide leading e-commerce platform. Results of an online A/B testing further verify the effectiveness of the proposed method FIVES, and we further provide FIVES as AI utilities for the customers of Alibaba Cloud.