Reviewerly/exHarmony huggingface dataset:
The Evaluation Method of English Teaching Ability in View of Internet of Things

This paper firstly investigates the current situation of English teaching evaluation research at home and abroad and the evaluation of basic English teaching through literature review. Most of the research involves personal experience summary, lack of academic, and empirical research proof. Research starting points are often very similar, with repetitive content. Most of these studies are limited to teaching evaluation or learning specific English knowledge, and the evaluation of teaching methods is limited. The article explains the importance and scope of English teaching evaluation research through the Internet of Things technology and explains that the guiding theories of this English teaching evaluation research are system theory, structure theory, and multiscientific theory. Finally, this paper conducts relevant experimental research and analyzes all aspects of English teaching. Through experiments, this paper finds that students’ input and level in collective or group English activities are both above 4.2 points. The self-assessment method obtained a score of 4.8 or more in the teacher’s evaluation. It is necessary and realistic to establish an independent teacher-student-centered teacher education evaluation system, to optimize the teacher’s beliefs of English teachers, to enrich and update the knowledge system of English teachers, to develop the practical wisdom of English teachers, to focus on students as the center of evaluation and decision-making, and to develop good teaching evaluation and decision-making habits.
Fall risk assessment through a synergistic multi-source DNN learning model

Falls are a complex problem and play a leading role in the development of disabilities in the older population. While fall detection systems are important, it is also essential to work on fall preventive strategies, which will have the most significant impact in reducing disability in the elderly. In this work, we explore a prospective cohort study, specifically designed for examining novel risk factors for falls in community-living older adults. Various types of data were acquired that are common for real-world applications. Learning from multiple data sources often leads to more valuable findings than any of the data sources can provide alone. However, simply merging features from disparate datasets usually will not produce a synergy effect. Hence, it becomes crucial to properly manage the synergy, complementarity, and conflicts that arise in multi-source learning. In this work, we propose a multi-source learning approach called the Synergy LSTM model, which exploits complementarity among textual fall descriptions together with people's physical characteristics. We further use the learned complementarities to evaluate fall risk factors present in the data. Experiment results show that our Synergy LSTM model can significantly improve classification performance and capture meaningful relations between data from multiple sources.
Research on intelligent operation and maintenance technology of urban rail transit based on cloud computing platform

At present, there are many problems in the operation and maintenance of equipment and facilities in the urban rail transit industry, such as untimely online monitoring, independent monitoring systems, low efficiency of traditional operation and maintenance and low level of intelligent operation and maintenance. Based on the analysis of the status of operation and maintenance application, mode and technology of the industry, this paper puts forward the overall technical framework of intelligent operation and maintenance of urban rail transit based on cloud platform. The paper also elaborates key technologies such as Internet of Things data monitoring, failure diagnosis, intelligent operation and maintenance, micro-services and cloud-side collaboration. And it has reference significance for improving the level of operation and maintenance management and security ability.
An Assessment of Spelling and Typographical Errors in WebOPAC of Academic Libraries

WebOPAC serves as a discovery tool to let users discover and ascertain the availability of an item in the stacks of a library. To make searching more efficient and effective there is a need to eliminate the typographical errors which may be present in library catalogue. Typographical errors in the bibliographic data are of high concern as it affects the discoverability of items. Similarly, the identification of erroneous records is a challenge in large bibliographic databases. The present work attempts to assess the typographical errors in the WebOPAC of Sayajirao Gaekwad Library of Banaras Hindu University using Terry Ballard's typographical error terms list. Simple random sampling is used to select a hundred terms from each section of Ballard's list. The WebOPAC was searched to find matched erroneous records. The findings of the study showed that out of 500 queried erroneous terms, 131 terms retrieved 889 records averaging an overall rate of 6.78 erroneous records per error. Moreover, a part-of-speech wise analysis showed that most errors in the bibliographic records were found to be in the case of nouns and proper nouns, while the least records were retrieved for technical terms. The study has implications for libraries wishing to improve the quality of the bibliographic data in the records of their WebOPACs.
A framework for mode classification in multimodal environments using radar-based sensors

Monitoring traffic at locations where bicyclists and pedestrians are present and studying their interactions with motorized vehicles has the potential to reveal underlying crash mechanisms and, in turn, guide the implementation of suitable countermeasures. Previous research has demonstrated the capabilities of vision-based systems in traffic monitoring; however, as these systems rely on video camera data, they remain constrained in adverse lighting and weather conditions. Although radar-based sensors can be used in traffic monitoring, they have not been tested in multimodal environments. To bridge this gap a novel framework to classify trajectories recorded by radar-based sensors in multimodal traffic environments is developed. The Support Vector Machine is used as the classifier and the following aspects allow to develop a robust, flexible, and transferable classification framework that can be applied in various traffic scenes. The SVM employs (1) a speed and length or speed and acceleration feature vector, (2) trajectory normalization scheme (i.e., select multiple measurements per trajectory), (3) training sample balancing strategy, and (4) cross-validation strategy. The framework is tested and validated using data from two different multimodal intersections. The results suggest that the mode type for every trajectory can be predicted with 95% accuracy using ten speed measurements and the vehicle’s mean length while accounting for the unequal number of observations per class. While these results are subject to change, i.e., both SVM’s input (feature vector and/or balancing approach) and performance may vary across different traffic scenes, the proposed framework is transferable and flexible.
Autonomous calibration for gaze detection using Bayesian estimation and canonical correlation analysis

For daily use of AR technology, the development of smart glasses which look like ordinary eyeglasses has been accelerated, and eye-tracking devices to support their video expression and natural human-machine interfaces have been attracting attention. Over the past few years, we have been developing a non-video-based eye-tracking system, that consists of a VCSEL array and a position-sensitive detector (PSD), and is implemented on small glass devices without preventing device design and their appearance. In wearable eye-tracking devices, calibration is frequently required, when misalignments of the device and the user switching occur. The most common calibration method is having the user gazes at multiple fixed points, but it interrupts user’s activities and causes stress. To eliminate the calibration stress, a novel algorithm has been proposed, that estimates the shape and position of user’s eyes from continuously detected data and corrects the gaze direction while wearing glasses. The fundamental principle of this algorithm is that such eye parameters affect spatial characteristics of detected laser beam spot on the PSD, reflected on the eye surface. The Bayesian estimation is used to updates the probability distribution of unconscious eye movements, and the eye parameters are identified with the help of the canonical correlation analysis. In this paper, the details of the gaze detection algorithm with autonomous calibration mechanism have been described, and a ray-tracing simulation has been performed for a proof of concept. The results show the applicability of our proposed algorithm to provide an eye-tracking module without any user stress of calibration.
Multi-scale features fused network with multi-level supervised path for crowd counting

Many CNN-based methods which utilize the density map to regress the count number of crowd are introduced to solve the crowd counting problem lately. Due to the head scale variations caused by the perspective change and background noise, these methods cannot address these two problems well in highly crowded scenario. In order to solve these two problems, we introduce a multi-scale features fused network with multi-level supervised path to produce the high-quality density map in this paper. Our model utilizes the first 13 layers of VGG16 model as the backbone, the multi-level supervised path in our model employs the multi-level dilated convolution module (MLD) to supervise the whole network at multi-level, and generate the attention map for the density map, which is used to handle the scale variations. The other path is used to fuse multi-scale features to generate the density map with soft spatial-channel attention module (SSCA) which aims to produce a saliency weight map of same size. In the end, the final density map is captured by the feature map multiply the attention map. In addition, a new objective function is proposed to train our network. A large number of experimental results show that compared with other networks, our method achieves better experimental results on four challenging datasets (UCF_CC_50, ShanghaiTech, UCF-QRNF and WorldExpo’10 dataset).
Named Entities as a Metadata Resource for Indexing and Searching Information

Named Entity Recognition (NER) is an essential task in Natural Language Processing (NLP). By using NER, it is possible to create associations in a text to recognize real-world entities. The data indexing process is also considered a vital resource, as it makes it easier to find texts in a set of documents. When we analyze a search engine, we aim at the ease of the user’s search process. Indexing recognized entities could help the search engine find data with a high semantic index, therefore, more accurate. This study aims to investigate the automatic transformation of annotated entities as indexes for a search engine. The recognition of entities used the hybrid model CRF+LG. Search engines usually work with keyword localization (tokens). However, this research aimed to use a semantic search, as it improves the quality of the results by understanding the user’s intention using enricher meta factors besides the keyword. We performed ten experiments using P@{5, 10, and 20} and the search engine with a high semantic index achieved accuracy of 100%, correctly returning all results. The search engine without NER was confused when producing results for person and organization categories, mainly.
Research on Oral English Dialogue Understanding Based on Deep Learning

Oral English dialogue is a crucial part of a dialogue system that enables a computer to “understand” the input language as a human does, so the performance of a dialogue system is closely related to the performance of oral English dialogue understanding. In task-based human-machine dialogue systems, external knowledge bases can provide the machine with valid information beyond the training data, helping the model to better perform the oral English dialogue comprehension task. In this paper, we propose a deep recurrent neural network based on feature fusion, which directly stacks multiple nodes at a single time node to deepen the complexity of nonlinear transformations. The feature fusion network structure is applied to the ATIS dataset for oral English dialogue comprehension experiments, and the experimental results demonstrate that the feature fusion RNN network can further improve the effectiveness of the RNN network and the GRU network structure unit can obtain better results among different RNN node units.
Application of Partial Differential Equation in Digital Image Processing Technology

In order to do a good job of digital image processing through partial differential equation, this paper will carry out relevant research, mainly taking image denoising, image repair as an example, discusses the application strategy of partial differential equation. The strategy in this paper can give full play to the role of partial differential equation, and complete image denoising and image restoration by digital image processing technology. The technical processing effect is good, and it can be applied widely and can be put into practice.
Hardware Implementation of Moving Object Detection using Adaptive Coefficient in Performing Background Subtraction Algorithm

Moving object detection is an essential process in many surveillance systems, autonomous navigation systems, and computer vision applications. A hardware architecture for the motion detection process based on the background subtraction operation and with the introduction of an adaptive background update coefficient is proposed. The architecture is implemented on a Kintex 7 FPGA device. Its operating frequency is 250 MHz for 360*640 video frame size and average processing time for each frame is 2.304 ms with 130 fps processing rate and its power consumption is 140 mW. The architecture achieves high speed performance with relatively low resource utilization.
Image Captioning based on Deep Convolutional Neural Networks and LSTM

Image captioning is a challenging task that needs the knowledge from both computer vision algorithms and language processing techniques. The model must be able to understand an image and then apply language generation techniques to describe an image in a natural language such as English. In this paper, we have presented an image captioning model which uses VGG16 for visual feature extraction and LSTM model to generate sentences corresponding to extracted visual features. We have performed experiments on Flickr8k and Flickr30k datasets. Bilingual Evaluation Understudy (BLEU) metric is used to measure the accuracy of the proposed model. The proposed model can be further extended to wide range of applications related to IOT based applications and smart control systems.
Performance Comparison of Equal Width and Equal Frequency Discretization Methods for Author’s Handwriting Recognition

Feature’s reformation or representation is a vital process to give impact towards the study of author’s handwriting recognition. This process is aimed to improve the recognition’s performance accuracy. The generation of features by some feature extraction methods usually resulted in a high dimensionality problem that led to some features redundancy and dependency among features that could lower recognition performance. This study implemented the Higher-Order United Moment Invariant (HUMI) feature extraction method to extract the features of the author’s handwriting image. The Equal Width (EW) and Equal Frequency (EF) Discretization methods are proposed to discretize the HUMI features. The EW discretization method reconstructs the HUMI features by reforming each feature with their representation values. These representation values are their unique general features for each bin. While the EF discretization method repositions the HUMI features into their bins based on the number of features frequency. Each bin will have almost the same number of feature values based on the determined range. As a result, the proposed EW discretization method has achieved the highest performance by classifier DTNB that performs at 99.91% for ten (10) cross-validation experiment setup while EF discretization method managed to reach at best 83% performance by classifier Naive Bayes with the same experimental setup. This study aims to compare the performance of both discretization methods and this shows that the EW discretization method has outperformed the EF discretization method. Nevertheless, both discretization methods have performed at the uppermost level and improve the performance of the author’s handwriting recognition.
Education in Cybersecurity

The article addresses education as the smartest investment in cybersecurity. One of the most intriguing findings is that 95% of security incidents involve human errors. Most security attacks are concerned with human weakness to attract victims and persuade them to give involuntary access to personal and sensitive information. To eliminate errors caused by social engineering and negligence and to increase users’ awareness of the threats, technologies and services should be combined with education. Education in the field of cybersecurity is a necessary consideration for both individuals and families, as well as for businesses, governments and educational institutions.&#x0D; For families and parents, the online safety of children is of major importance. Equally essential is the protection of information that might affect your personal finances, and precious family assets, such as photos, videos etc.&#x0D; For educational institutions, it is important to understand the link between the online world and the "real" one. Teachers, staff, students, tutors, pupils, etc. should be trained in appropriate on-line behavior to reduce vulnerabilities and create a safer online environment.&#x0D; A better awareness through security education can help enterprises protect their intellectual property and ensure availability of services.&#x0D; Governments hold an enormous amount of personal data and records of their citizens, as well as confidential government information, which most often serves as a target for attack. Only through education and awareness, the confidence in public services can be gained. Cybersecurity depends on education.
Temporary ambiguity and memory for the context of spoken language

Spoken language is interpreted incrementally, with listeners considering multiple candidate meanings as words unfold over time. Due to incremental interpretation, when a speaker refers to something in the world, there is often temporary ambiguity regarding which of several candidate items in the referential context the speaker is referring to. Subsequent tests of recognition memory show that listeners have good memory for referenced items, but that listeners also sometimes recognize non-referenced items from the referential context that share features with items that were mentioned. Predicted or inferred (but not experienced) interpretations of what was said are also sometimes retained in memory. While these findings indicate that multiple items from the referential context may be encoded in memory, the mechanisms supporting memory for the context of language use remain poorly understood. This paper tests the hypothesis that a consequence of temporary ambiguity in spoken language is enhanced memory for the items in the referential context. Two experiments demonstrate that periods of temporary referential ambiguity boost memory for non-referenced items in the referential context. Items that temporarily matched the unfolding referring expression were better remembered than those that did not. The longer the period of ambiguity, the stronger the memory boost, particularly for items activated early in the expression. In sum, the fact that spoken language unfolds over time creates momentary ambiguity about the speaker's intention; this ambiguity, in turn, allows the listener to later remember not only what the speaker did say, but also what they could have, but did not.
Detection of Phishing Websites Using Machine Learning

Criminals seeking sensitive information construct illegal clones of actual websites and e-mail accounts. The e-mail will be made up of real firm logos and slogans. When a user clicks on a link provided by these hackers, the hackers gain access to all of the user&#x0027;s private information, including bank account information, personal login passwords, and images. Random Forest and Decision Tree algorithms are heavily employed in present systems, and their accuracy has to be enhanced. The existing models have low latency. Existing systems do not have a specific user interface. In the current system, different algorithms are not compared. Consumers are led to a faked website that appears to be from the authentic company when the e-mails or the links provided are opened. The models are used to detect phishing Websites based on URL significance features, as well as to find and implement the optimal machine learning model. Logistic Regression, Multinomial Naive Bayes, and XG Boost are the machine learning methods that are compared. The Logistic Regression algorithm outperforms the other two.
Cryptocurrency – The Next Big Thing

The cryptocurrencies are a hot topic in the global financial system. Cryptocurrency is a digital or virtual or internet currency that uses cryptography for security. Cryptocurrency has created unmatched changes in the financial market having both positive and negative contributions. The concept of cryptocurrency is a little hard to accept, but it is easy to use. It is considered difficult because it is entirely different from our conventional currencies that we people are using since ages. Here, we focus the different types of cryptocurrencies, origin and evolution of the term. The role of cryptography in early cryptocurrencies, Issues currently associated with the term, the role of cryptography in today’s cryptocurrencies, cryptocurrencies exchanges, Cryptocurrencies Trading, advantages and disadvantages of cryptocurrencies trading, How Many cryptocurrencies are there? Market Capitalization of Cryptocurrency, the 2021 Global Crypto Adoption Index Top 20, One Year change in the value of Crypto Assets.
Social Media Recommender Systems (SMRS): A Bibliometric Analysis Study 2000–2021

The increasing popularity of social media resources such as blogs, bookmarks, chatrooms, forums and video portals in recent years has attracted diverse users.Following the rise of the Internet, online content has become overloaded, prompting the introduction of recommender systems on social media.As a result, research on the dynamic growth of recommender systems in social media has gained significant traction since the year 2000.Social media recommender systems (SMRS) utilize multiple recommendation fields such as item, user, location, tag, event, tour and game in searching for preferred recommended information.Thus, young research fellows, academic scholars and practitioners must understand the need for SMRS to be complemented with recommendation fields.This requirement underlines the significance of a bibliometric analysis that focuses on social media based on existing publications.Hence, using the Web of Science (WoS) database, this study aimed to gather statistical information on SMRS to help researchers acquire an extensive understanding of such systems.The analysis was conducted by identifying SMRS-related publications and scientometric indicators to assess the growth rate, including the relative growth rate (RGR), doubling time (DT) and the field-normalized citation score (NCSf)-for citation analysis.Overall, this bibliometric study provides relevant measures for comparing and improving the citation rate of publications for new researchers.