Reviewerly/exHarmony huggingface dataset:
Resonant Coupling Parameter Estimation with Superconducting Qubits

Calibrating a quantum computer should not require days or weeks: A method is presented to accurately estimate quantum parameters and evade noise much more efficiently than with any state-of-the-art technique.
WSAMLP: Water Strider Algorithm and Artificial Neural Network-based Activity Detection Method in Smart Homes

One of the crucial applications of IoT is developing smart cities via this technology. Smart cities are made up of smart components such as smart homes. In smart homes, a variety of sensors are used for making the environment smart, and the smart things, in such homes, can be used for detecting the activities of the people inside them. Detecting the activities of the smart homes’ users may include the detection of activities such as making food or watching TV. Detecting the activities of residents of smart homes can tremendously help the elderly or take care of the kids or, even, promote security issues. The information collected by the sensors could be used for detecting the kind of activities; however, the main challenge is the poor precision of most of the activity detection methods. In the proposed method, for reducing the clustering error of the data mining techniques, a hybrid learning approach is presented using Water Strider Algorithm. In the proposed method, Water Strider Algorithm can be used in the feature extraction phase and exclusively extract the main features for machine learning. The analysis of the proposed method shows that it has precision of 97.63 %, accuracy of 97. 12 %, and F1 index of 97.45 %. It, in comparison with similar algorithms (such as Butterfly Optimization Algorithm, Harris Hawks Optimization Algorithm, and Black Widow Optimization Algorithm), has higher precision while detecting the users’ activities.
PEFT-based Trade-off Schedule Plan for Executing IoT Applications in Cloud Environment

Internet of Things (IoT) consists of physical things or objects that are connected through a network of sensors, software, and electronics. Mainly, IoT devices are of restricted processing and storage capacities. On the other hand, infinite storage and processing capacities are available over the cloud. Thus by merging these two methods, the mutual advantages have been discussed in the past years. Due to heterogeneous resource requirements of IoT application’s task, it is not so easy to schedule these over the cloud. Existing heuristic-based scheduling strategies had mainly developed considering either minimization of makespan or minimization of cost. But, for the present scenario, an efficient schedule plan is required to schedule IoT applications over cloud that minimizes the execution time along with execution cost while preserving the task dependencies constraints as well as user-defined constraints like budget and deadline. In this paper, Cost–time computational intelligent heuristics based upon PEFT i.e. CTPEFT has been proposed to give a trade-off schedule plan between computational cost and time beneath the user-specified deadline and budget constraints. To validate the effectiveness of the proposed heuristic, extensive simulation experiments have been conducted while considering different synthetic IoT application tasks. CTPEFT has been compared with our previously proposed algorithm, i.e. BDHEFT as well as with other competing algorithms like BHEFT, and HCPPEFT. Simulation results shows that CTPEFT heuristic can generate better cost-makespan trade-off plan as compared to BDHEFT, BHEFT, and HCPPEFT.
Hiragana and Katakana Minutiae based Recognition System

The Japanese writing system is unique due to the number of characters employed and the methods used to write words.It consists of three different 'alphabets', which may result in the methods used to process Latin script not being sufficient to obtain satisfactory results when attempting to apply them to a recognition of the Japanese script.The authors present an algorithm based on minutiae, i.e., feature points, to recognise the hiragana and katakana characters.A method using image processing algorithms is compared with a method using a neural network for the purpose of automating this process.Based on the distribution and type of minutiae, vectors of features have been created to recognise 96 different characters.The authors conducted a study showing the effect of the chosen segmentation method on the accuracy of the character recognition.The proposed solution has achieved a maximum accuracy at the level of 65.2%.
Data Storage Security in Mobile Cloud Computing (MCC) using Improved Blowfish Algorithm

Mobile cloud computing (MCC) combines cloud computing and mobile computing to deliver vast computational resources to mobile consumers, network operators, and cloud computing providers. You may access your data from anywhere in the globe using any mobile device that is linked to the Internet. Cloud computing provides access to data in real-time whenever and wherever want. Any conventional mobile device can benefit from MCC's infrastructure, computational capacity, software, and platform services. Network security, web application security, data access, authentication, authorization, data confidentiality, and data breach are all concerns of MCC's security. Because mobile devices lack sufficient storage and processing power, their data storage capacity is limited. Users of mobile devices may inadvertently provide sensitive information over the network or through the application. Therefore, data security is the main concern for mobile device users. The objective of this paper is to find a solution that can enhance technical requirements with relation to user’s data security and privacy in mobile cloud computing. To achieve this improved blowfish encryption algorithm is used to encrypt each user’s data security and where the shared secret key is hash down using message digest called secured hash function. Hashing can increase the integrity and privacy of user data. The proposed algorithm is evaluated with a normal blowfish algorithm and 3DES with different parameters. Improved blowfish algorithm shows better performance than normal blowfish algorithm and 3DES. In this work, we have developed web-based application where the Amazon MySQL RDS database is used for data storage.
An adaptive fringe projection method for measuring three-dimensional profile of surface with high reflectivity using sub-pixel coordinate mapping

Fringe projection profilometry (FPP) is a popular three-dimensional (3D) shape measurement technique and has been widely used in industry. However, measuring surface with high reflectivity is still challenging because image saturation leads to absolute phase errors and reconstruction errors. In this paper, a novel adaptive fringe projection method is proposed. First, a projection intensity model is built. Adjusting the exposure time only once can easily calculate the low projection intensity. Then, using fringe patterns with low intensity we can get the coordinate mapping relationship. By mapping and filtering the sub-pixel coordinates in the projector image, we can obtain more accurate coordinate correspondence and gray value correspondence of the projector and the camera. Finally, the parameters of the projection intensity model are fitted to calculate the optimal intensity and generate accurate adaptive fringe patterns. The measurement experiments demonstrate that the proposed method is effective in obtaining high-precision and complete 3D profile of surface with high reflectivity.
Application of Artificial Intelligence Technology in Electronic Information Engineering

Abstract In the development process of artificial intelligence, electronic information engineering technology is indispensable. Electronic information engineering technology support is extremely important for the development of artificial application. This paper describes the related concepts of artificial intelligence and electronic information engineering, and researches and designs the application of artificial intelligence technology in the electronic information engineering platform. Through the analysis, we can see that the system is not There are fatal loopholes, the success rate of data transmission basically meets the demand of more than 80%, and the response time is within 3S, which can also meet the set requirements.
Assessing academic English for higher education admissions. edited by XiaomingXi and John M.Norris, Routledge: New York, 2021, xii + 219pp. ISBN: 978‐0‐8153‐5064‐4

International Journal of Applied LinguisticsEarly View BOOK REVIEW Assessing academic English for higher education admissions. edited by Xiaoming Xi and John M. Norris, Routledge: New York, 2021, xii + 219pp. ISBN: 978-0-8153-5064-4 Yongxiang Yang, Corresponding Author Yongxiang Yang yongxiang1016@126.com orcid.org/0000-0002-4760-2856 Ocean University of China - College of Foreign Languages, Qingdao, Shandong, China Correspondence Yongxiang Yang, Ocean University of China - College of Foreign Languages, No.238 Songling Road (OUC Laoshan Campus), Qingdao, Shandong 266100, China. Email: yongxiang1016@126.comSearch for more papers by this author Yongxiang Yang, Corresponding Author Yongxiang Yang yongxiang1016@126.com orcid.org/0000-0002-4760-2856 Ocean University of China - College of Foreign Languages, Qingdao, Shandong, China Correspondence Yongxiang Yang, Ocean University of China - College of Foreign Languages, No.238 Songling Road (OUC Laoshan Campus), Qingdao, Shandong 266100, China. Email: yongxiang1016@126.comSearch for more papers by this author First published: 26 November 2021 https://doi.org/10.1111/ijal.12405Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Early ViewOnline Version of Record before inclusion in an issue RelatedInformation
An Analysis of the Difficulties in the Application of Information Technology in Credit Training at Public Universities in Vietnam

research-article An Analysis of the Difficulties in the Application of Information Technology in Credit Training at Public Universities in Vietnam Share on Authors: Huong Thi Thu Dao FPT University, Viet Nam FPT University, Viet NamView Profile , Thanh Hoang FPT University-FPT Polytechnic, Viet Nam FPT University-FPT Polytechnic, Viet NamView Profile Authors Info & Claims ICETT 2021: 2021 7th International Conference on Education and Training TechnologiesApril 2021 Pages 12–19https://doi.org/10.1145/3463531.3463534Online:20 July 2021Publication History 0citation13DownloadsMetricsTotal Citations0Total Downloads13Last 12 Months13Last 6 weeks2 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
The 2021 Bitcoin Bubbles and Crashes – Detection and Classification

In this study, we adopted the Log-Periodic Power Law Singularity (LPPLS) model for real-time identification and monitoring of Bitcoin bubbles and crashes using different time scale data and proposed the modified Lagrange regularization method to alleviate the impact of potential LPPLS model over-fitting to better estimate bubble start time and market regime change. We also aimed to determine the natures of the bubbles and crashes – be it endogenous due to its own price evolution or exogenous due to external market and/or policy influences. We performed a systematic market event analysis and correlated which to Bitcoin bubbles detected. Based on the daily LPPLS confidence indictor from December 1, 2019 to June 24, 2021, we found that the Bitcoin boom from November 2020 to mid-January 2021 is an endogenous bubble, stemming from the self-reinforcement of cooperative herding and imitative behaviors of market players, while the price spike from mid-January 2021 to mid-April 2021 is likely an exogenous bubble driven by extrinsic events including a series of large-scale acquisitions and adoptions by well-known institutions such as Visa and Tesla. We have also demonstrated the utilities of multi-resolution LPPLS analysis in revealing both short-term changes and long-term states.
Fast Filtering of Search Results Sorted by Attribute

Modern search services often provide multiple options to rank the search results, e.g., sort “by relevance”, “by price” or “by discount” in e-commerce. While the traditional rank by relevance effectively places the relevant results in the top positions of the results list, the rank by attribute could place many marginally relevant results in the head of the results list leading to poor user experience. In the past, this issue has been addressed by investigating the relevance-aware filtering problem, which asks to select the subset of results maximizing the relevance of the attribute-sorted list. Recently, an exact algorithm has been proposed to solve this problem optimally. However, the high computational cost of the algorithm makes it impractical for the Web search scenario, which is characterized by huge lists of results and strict time constraints. For this reason, the problem is often solved using efficient yet inaccurate heuristic algorithms. In this article, we first prove the performance bounds of the existing heuristics. We then propose two efficient and effective algorithms to solve the relevance-aware filtering problem. First, we propose OPT-Filtering, a novel exact algorithm that is faster than the existing state-of-the-art optimal algorithm. Second, we propose an approximate and even more efficient algorithm, ϵ-Filtering, which, given an allowed approximation error ϵ, finds a (1-ϵ)–optimal filtering, i.e., the relevance of its solution is at least (1-ϵ) times the optimum. We conduct a comprehensive evaluation of the two proposed algorithms against state-of-the-art competitors on two real-world public datasets. Experimental results show that OPT-Filtering achieves a significant speedup of up to two orders of magnitude with respect to the existing optimal solution, while ϵ-Filtering further improves this result by trading effectiveness for efficiency. In particular, experiments show that ϵ-Filtering can achieve quasi-optimal solutions while being faster than all state-of-the-art competitors in most of the tested configurations.
Optimization of the Study Materials Design in the Conditions of the COVID-19 Pandemic

Abstract Numerical and statistical methods are frequently used as a research tool in technical fields and therefore are an important part of studies within technical study programmes. The COVID-19 pandemic has affected university education resulting in the distance form of education. Experience has shown that to train the application of numerical and statistical methods as well as to create homework, which is a condition for students to complete their courses, it is important to provide a large number of exercises and tasks. The work describes a model of automated study materials design using computer technology with emphasis on optimizing the design in terms of three optimization conditions: any number of exercises and tasks, minimizing the design time required and a sufficient variety of exercises and tasks. Free Pascal programming language and the LaTeX typographic system were used as the basis for the model.
The Research of English Teaching Corpus Construction Based on Internet and Information Technology

College English teaching in the Internet era is characterized by its long-term, extensive, instrumental and valuable nature, which provides a carrier and educational atmosphere for the implementation of "English Course Teaching". Computer aided translation software and corpus technology are increasingly used in translation field at home and abroad. However, most domestic scholars have not applied corpus to the development and construction of college English websites. Therefore, this paper puts forward a study on the construction of English teaching corpus. In view of the current situation of the construction of English teaching corpus, this paper analyzes the numerical changes of the specialty, timeliness and flexibility of English teaching corpus by analyzing data. According to the analysis results, English corpus teaching is superior to traditional English teaching in terms of professionalism, timeliness and flexibility. And by data analysis of the degree of satisfaction after the construction of the English teaching corpus, the data show that the degree of satisfaction after the construction of the English teaching corpus is also increasing year by year. The development of English teaching corpus in college English teaching has broken through traditional English teaching limits and enhanced students' love for English and Chinese traditional culture.
A System for Scientific Papers Recommendation and Search

Searching information by keywords is a simple and effective method used for a long time. Unfortunately, its usefulness is not well exploited in certain contexts. Indeed, in the case of scholarly resources (scientific papers, thesis, courses, etc.) what is generally done is a classification of documents by field (speciality), or by category (thesis, course materials, etc.). We consider the context of a user (student/teacher) who wants to search for resources on a particular field, but he does not know where to look, we consider also the context of a researcher who wants to search scientific papers. The existence of a dedicated search engine is thus useful in this case of problem. The purpose of this paper is to present an efficient engine of academic and scientific documents and more specifically a function to weight documents in the context of academic resources. Our approach is based on a multi-criteria weighting method that uses the average document score, the Okapi BM25 score and the number of downloads of documents. Alerts can also be programmed to keep the user informed about new publications.
Design of CNNs with adaptive activation function and application in fault detection

Activation function plays an important role in deep convolutional neural network. It realizes the nonlinear mapping of eigenspace. The traditional activation function is a fixed form in the network model. In this paper, the methods of linear combination activation functions and gated adaptive activation functions are proposed to learn the activation function. By combining basic activation functions and using gating ideas to allow activation functions to adapt to the input. Parameters are learned through back propagation. Using the learning parameters, a nonlinear mapping suitable for the specific data distribution is established. Compared with the traditional activation function, more complex nonlinear representation or linear representation can be obtained, which can help a network model more suitable for input data. Moreover, the proposed gated activation function can adjust the form of the activation function according to the specific input, which can increase the ability of feature extraction and generalization of network model. Experiments show that the proposed adaptive CNN model has better performance in fault detection.
Interpretable Aspect-Aware Capsule Network for Peer Review Based Citation Count Prediction

Citation count prediction is an important task for estimating the future impact of research papers. Most of the existing works utilize the information extracted from the paper itself. In this article, we focus on how to utilize another kind of useful data signal (i.e., peer review text) to improve both the performance and interpretability of the prediction models. Specially, we propose a novel aspect-aware capsule network for citation count prediction based on review text. It contains two major capsule layers, namely the feature capsule layer and the aspect capsule layer, with two different routing approaches, respectively. Feature capsules encode the local semantics from review sentences as the input of aspect capsule layer, whereas aspect capsules aim to capture high-level semantic features that will be served as final representations for prediction. Besides the predictive capacity, we also enhance the model interpretability with two strategies. First, we use the topic distribution of the review text to guide the learning of aspect capsules so that each aspect capsule can represent a specific aspect in the review. Then, we use the learned aspect capsules to generate readable text for explaining the predicted citation count. Extensive experiments on two real-world datasets have demonstrated the effectiveness of the proposed model in both performance and interpretability.
Influence of Augmented Reality for the learning of Geometric Solids in Primary School students

Currently, technological advances have had relevance in different factors of daily life, however, in many educational centers it has not been used for the benefit of education. The objective of this research was to influence the learning process of Geometric Solids through the use of a mobile application. The development of the application was proposed under the Mobile-D methodology and the name “ARmetry” was proposed. The experimental design of the quasi-experimental type was also chosen, where the population to be studied was 140 students and the sample was two sections of the morning shift, 70 students for the sample in total. Finally, it is concluded that using a mobile application with Augmented Reality positively influences the learning process in Geometric Solids.
High-speed fringe projection profilometry based on convolutional neural network

Aiming at the serious noise and low signal-to-noise ratio of the fringe images obtained by industrial cameras under high-speed projection, a high-speed fringe projection measurement method based on convolutional neural network is proposed, which can achieve high-quality phase recovery and high-precision three-dimensional reconstruction in high-speed scenes. Using the designed convolutional neural network, the noise fringe images obtained at high frame rate and the wrapped phase images recovered by the traditional 12-step phase-shifting method at low frame rate are input into the convolutional neural network for training. After learning the mapping relationship between a large number of noise fringe images in the data set and the corresponding high-quality wrapped phase, a trained network model is obtained. And using this model, the high-quality wrapped phase information can be directly recovered from the input noise fringe images. The experiment results demonstrate that the method proposed in this paper can achieve with an accuracy of about 32&mu;m through three noise fringe images at the camera frame rate of 700 frames per second.
COVID-19 EFFECT: FROM FACE-TO-FACE TEACHING TO FORCED DUAL TEACHING. TOLERANCE OF UNIVERSITY STUDENTS TO THE CHANGE IN THE SYSTEM

COVID-19 EFFECT: FROM FACE-TO-FACE TEACHING TO FORCED DUAL TEACHING. TOLERANCE OF UNIVERSITY STUDENTS TO THE CHANGE IN THE SYSTEM