Reviewerly/exHarmony huggingface dataset:
RETRACTED: Mechanism of vocational education promoting precision poverty alleviation

The purpose of this study is to study in depth the effective mechanism of China's vocational education reform to promote the poverty alleviation of precision industries. The purpose of this research is to deeply study the effective mechanism of my country's vocational education reform to promote precision industry poverty alleviation, and investigate the financial aid for poor students and the number of aids of a secondary vocational college for 5 consecutive years. Investigate the number of registered poor agricultural households in a city for five consecutive years, the number of rural labor transfers in employment, and the growth of per capita income of farmers. Taking 100 poor families in a poverty-stricken county as an example to investigate and analyze the educational background, knowledge level and attitudes of parents and students to school. Investigate various major local technical trainings conducted in a city for 5 consecutive years. The results show that the poverty subsidy of vocational schools has increased year by year, and the number of poverty subsidies has also increased. Among them, the intensity of targeted poverty alleviation has also been continuously strengthened. The dynamic management of information on the establishment of filing cards has greatly improved the precision of poverty alleviation and realized dynamic management. Per capita income has also achieved steady growth. At the same time, most parents have a junior high school diploma, and the student's academic qualifications are generally not high. In view of the attitude to school, most parents believe that it is very necessary, but the students think that they can not go up. There are more and more people participating in the main technical training in various localities, and more and more training projects are taking place, and the proportion is gradually increasing. These jobs are of great significance to the development of today's society.
Blind channel estimation for continuous‐variable quantum key distribution

Quantum EngineeringVolume 2, Issue 3 e46 RESEARCH HIGHLIGHT Blind channel estimation for continuous-variable quantum key distribution Yongmin Li, Corresponding Author yongmin@sxu.edu.cn orcid.org/0000-0003-0228-7693 State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, P. R. China Correspondence Yongmin Li, State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan 030006, P. R. China. Email: yongmin@sxu.edu.cnSearch for more papers by this author Yongmin Li, Corresponding Author yongmin@sxu.edu.cn orcid.org/0000-0003-0228-7693 State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan, P. R. China Correspondence Yongmin Li, State Key Laboratory of Quantum Optics and Quantum Optics Devices, Institute of Opto-Electronics, Shanxi University, Taiyuan 030006, P. R. China. Email: yongmin@sxu.edu.cnSearch for more papers by this author First published: 28 May 2020 https://doi.org/10.1002/que2.46Citations: 3Read 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 onEmailFacebookTwitterLinked InRedditWechat No abstract is available for this article.Citing Literature Volume2, Issue3September 2020e46 RelatedInformation
When Pigs Fly: Contextual Reasoning in Synthetic and Natural Scenes

Context is of fundamental importance to both human and machine vision; e.g., an object in the air is more likely to be an airplane than a pig. The rich notion of context incorporates several aspects including physics rules, statistical co-occurrences, and relative object sizes, among others. While previous work has focused on crowd-sourced out-of-context photographs from the web to study scene context, controlling the nature and extent of contextual violations has been a daunting task. Here we introduce a diverse, synthetic Out-of-Context Dataset (OCD) with fine-grained control over scene context. By leveraging a 3D simulation engine, we systematically control the gravity, object co-occurrences and relative sizes across 36 object categories in a virtual household environment. We conducted a series of experiments to gain insights into the impact of contextual cues on both human and machine vision using OCD. We conducted psychophysics experiments to establish a human benchmark for out-of-context recognition, and then compared it with state-of-the-art computer vision models to quantify the gap between the two. We propose a context-aware recognition transformer model, fusing object and contextual information via multi-head attention. Our model captures useful information for contextual reasoning, enabling human-level performance and better robustness in out-of-context conditions compared to baseline models across OCD and other out-of-context datasets. All source code and data are publicly available at https://github.com/kreimanlab/WhenPigsFlyContext
Assessing mobile learning system performance in Indonesia: Reports of the model development and its instrument testing

It is undeniable that people life patterns and technological developments are interrelated within a supply and demand cycle. In the education world, the emergence of the internet and mobile technologies has opened the learning boundaries through the use of mobile learning (m-learning). In Indonesia, the learning service industry has been begun to enliven the outside school education sector for almost five years ago. Even though the learning has been discussed around a decade ago, however, it is still rare studies that discuss the performance of the m-learning system based on the end-user perceptions in particular. Therefore, the study may still indispensable, especially from the perspectives of a developing nation. This paper elucidates the preliminary stage results of the above-mentioned study, including the results of the model development and its instrument testing. The DeLone and Mclean information system (IS) success model was adopted, combined with the individual motivation and organizational culture theories, and then adapted into the processional and causal logic of the success model. Around 50 respondent data were collected online and processed and analyzed based on the outer model assessments of the PLS-SEM method using SmartPLS 3.0 to know the reliability and validity of each indicator. The result shows that two of 31 are rejected indicators. The rejections may be the revision considerations for the next study stages. Although this may be trivial for experts, the clarity of its methodological explanations may guide the novice researchers, how to develop a research model and its instrument testing.
Using Amazon MTurk for Research in Academia: A Beginner’s Guide for Using Qualtrics, Detecting VPN/ Proxy, Limiting Countries Using Geolocation &amp;amp; Other Tips

This is beginner's guide for using MTurk. If you intend to use MTurk for your research in academia using Qualtrics, this has details and tips which will allow you to conduct a study. The information given is based on our experiences conducting MTurk studies in summer of 2019. Overall, the quality of the respondents was very good. This paper contains suggestions and tips on setting criteria, using VPN/ proxy detectors, checking for location and ensuring you can conduct an MTurk study reliably and efficiently.
RETRACTED: Study on the Mixed English Teaching Model in Higher Vocational Colleges Under the Background of Big Data

Abstract This paper selects the reform of English course teaching mode in higher vocational colleges(HVC) as the research object, and studies how to make full use of big data(BD) technology to build a teaching platform to integrate teaching resources. In addition, we analyze the learning behavior of students and predict student learning effects. On this basis, a diversified and mixed English teaching mode in HVC is constructed, which enriches the theory of higher vocational English teaching and promotes the continuous improvement of the quality of higher vocational English teaching. What’s more, it ensures that students’ English ability is fully exercised and better adapted to the future society.
Estimating Crowd Size for Public Place Surveillance Using Deep Learning

With the overwhelming speed of population outbursts throughout the world, it raises a security concern over public places like supermarkets, offices, banks, political rallies, religious events etc. The risk of any abnormal behavior or any security concern arises with the number of people in the crowd. Most of the public places are overcrowded and require crowd count monitoring to avoid any mishappening. It is impossible to appoint personnel at different locations to count people over there. The role of CCTV cameras is pertinent as far as remote monitoring of crowds over public places is concerned, but it is a cumbersome job for a person to count people in a crowd only by monitoring multiple videos of different locations at a time. Also, with the help of CCTV footage it is not an easy task to manage crowds, specifically if it is a dense crowd. Automated crowd count that can estimate the total number of people in a crowd image is needed for the hour. In this chapter, we have reviewed crowd count methods using state of art deep learning models for automated crowd count and their performance analysis on major crowd counting datasets.
Frameworks for Querying Databases Using Natural Language

A natural language interface is useful for a wide range of users to retrieve their desired information from databases without requiring prior knowledge of database query language such as SQL. The advent of user-friendly technologies, such as speech-enabled interfaces, have revived the use of natural language technology for querying databases; however, the most relevant and last work presenting state of the art was published back in 2013 and does not encompass several advancements. In this paper, the authors have reviewed 47 frameworks that have been developed during the last decade and categorized the SQL and NoSQL-based frameworks. Furthermore, the analysis of these frameworks is presented on the basis of criteria such as supporting language, scheme of heuristic rules, interoperability support, scope of the dataset, and overall performance score. The study concludes that the majority of frameworks focus on translating natural language queries to SQL and translates English language text to queries.
Author Correction: An image encryption scheme based on public key cryptosystem and quantum logistic map

An amendment to this paper has been published and can be accessed via a link at the top of the paper.
Meta-Learning for Effective Multi-task and Multilingual Modelling

Natural language processing (NLP) tasks (e.g.question-answering in English) benefit from knowledge of other tasks (e.g., named entity recognition in English) and knowledge of other languages (e.g., question-answering in Spanish).Such shared representations are typically learned in isolation, either across tasks or across languages.In this work, we propose a meta-learning approach to learn the interactions between both tasks and languages.We also investigate the role of different sampling strategies used during meta-learning.We present experiments on five different tasks and six different languages from the XTREME multilingual benchmark dataset (Hu et al., 2020).Our meta-learned model clearly improves in performance compared to competitive baseline models that also include multitask baselines.We also present zero-shot evaluations on unseen target languages to demonstrate the utility of our proposed model.
Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents

We address the black-box issue of VR sickness assessment (VRSA) by evaluating the level of physical symptoms of VR sickness. For the VR contents inducing the similar VR sickness level, the physical symptoms can vary depending on the characteristics of the contents. Most of existing VRSA methods focused on assessing the overall VR sickness score. To make better understanding of VR sickness, it is required to predict and provide the level of major symptoms of VR sickness rather than overall degree of VR sickness. In this paper, we predict the degrees of main physical symptoms affecting the overall degree of VR sickness, which are disorientation, nausea, and oculomotor. In addition, we introduce a new large-scale dataset for VRSA including 360 videos with various frame rates, physiological signals, and subjective scores. On VRSA benchmark and our newly collected dataset, our approach shows a potential to not only achieve the highest correlation with subjective scores, but also to better understand which symptoms are the main causes of VR sickness.
Detection and Prevention of Spam Mail with Semantics-based text classification of Collaborative and Content Filtering

Abstract Eventhough, conventional technologies are quiet good in separating spam messages, still soo many measures have to be considered to make more accuracy in spam filtering. In this work, we worked towards detecting spam mails and filtering it during its transmission. We proposed Collaborative filtering approach hybrid with text classification (semantics based). The related feature are retrieved from the text content. Also, another filtering method known as Content-based filtering is proposed which filters the same spam mail with more precision and better accuracy. Along with the semantic texts the Content-based filtering filters the special symbols such as HTML tags, @,/etc. Results are compared and the accuracy of detecting spam e-mails of Content-based filters is more than that of Collaborative filters. Both Collaborative and Content-based filters perform keyword check available in the spam keyword database and detects whether the mail sent by the sender is genuine or spam. Genuine emails are sent successfully and the spam emails are blocked at the server side. Content-based email classification requires an understanding of both structural and semantic attributes of email. Conventional research is focussed on semantic properties through structural components of email. After analysing the emails as events (as a major subset of the class of email), a rich contextual test-bed representation for an understanding of the semantic attributes of emails has been devised. The event-based emails have traditionally been studied based on simple structural properties.
Attention-Guided Digital Adversarial Patches on Visual Detection

Deep learning has been widely used in the field of image classification and image recognition and achieved positive practical results. However, in recent years, a number of studies have found that the accuracy of deep learning model based on classification greatly drops when making only subtle changes to the original examples, thus realizing the attack on the deep learning model. The main methods are as follows: adjust the pixels of attack examples invisible to human eyes and induce deep learning model to make the wrong classification; by adding an adversarial patch on the detection target, guide and deceive the classification model to make it misclassification. Therefore, these methods have strong randomness and are of very limited use in practical application. Different from the previous perturbation to traffic signs, our paper proposes a method that is able to successfully hide and misclassify vehicles in complex contexts. This method takes into account the complex real scenarios and can perturb with the pictures taken by a camera and mobile phone so that the detector based on deep learning model cannot detect the vehicle or misclassification. In order to improve the robustness, the position and size of the adversarial patch are adjusted according to different detection models by introducing the attachment mechanism. Through the test of different detectors, the patch generated in the single target detection algorithm can also attack other detectors and do well in transferability. Based on the experimental part of this paper, the proposed algorithm is able to significantly lower the accuracy of the detector. Affected by the real world, such as distance, light, angles, resolution, etc., the false classification of the target is realized by reducing the confidence level and background of the target, which greatly perturbs the detection results of the target detector. In COCO Dataset 2017, it reveals that the success rate of this algorithm reaches 88.7%.
Bounding the Forward Classical Capacity of Bipartite Quantum Channels

We introduce various measures of forward classical communication for bipartite quantum channels. Since a point-to-point channel is a special case of a bipartite channel, the measures reduce to measures of classical communication for point-to-point channels. As it turns out, these reduced measures have been reported in prior work of Wang et al. on bounding the classical capacity of a quantum channel. As applications, we show that the measures are upper bounds on the forward classical capacity of a bipartite channel. The reduced measures are upper bounds on the classical capacity of a point-to-point quantum channel assisted by a classical feedback channel. Some of the various measures can be computed by semi-definite programming.
The two-pass sliding innovation smoother

The sliding innovation filter (SIF) is a newly developed filter that shares similar principles with sliding mode observers and variable structure techniques. The SIF is formulated as a predictor-corrector method that uses the innovation or measurement error as a switching hyperplane and forces the states to remain within a region of its state trajectory. In this brief paper, the SIF is reformulated as a two-pass smoother to reduce the effects of noise and improve the overall performance. The proposed method, known as the sliding innovation smoother (SIS), is applied on an aerospace flight surface actuator, and the results are compared to the original filter.
A Comparative Study of Stochastic Optimizers for Fitting Neuron Models. Application to the Cerebellar Granule Cell

This work compares different algorithms to replace the genetic optimizer used in a recent methodology for creating realistic and computationally efficient neuron models. That method focuses on single-neuron processing and has been applied to cerebellar granule cells. It relies on the adaptive-exponential integrate-and-fire (AdEx) model, which must be adjusted with experimental data. The alternatives considered are: i) a memetic extension of the original genetic method, ii) Differential Evolution, iii) Teaching-Learning-Based Optimization, and iv) a local optimizer within a multi-start procedure. All of them ultimately outperform the original method, and the last two do it in all the scenarios considered.
Strongly universally consistent nonparametric regression and classification with privatised data

In this paper we revisit the classical problem of nonparametric regression, but impose local differential privacy constraints. Under such constraints, the raw data (X1,Y1),...,(Xn,Yn), taking values in Rd×R, cannot be directly observed, and all estimators are functions of the randomised output from a suitable privacy mechanism. The statistician is free to choose the form of the privacy mechanism, and here we add Laplace distributed noise to a discretisation of the location of a feature vector Xi and to the value of its response variable Yi. Based on this randomised data, we design a novel estimator of the regression function, which can be viewed as a privatised version of the well-studied partitioning regression estimator. The main result is that the estimator is strongly universally consistent, and we further establish an upper bound on the rate of convergence. Our methods and analysis also give rise to a strongly universally consistent binary classification rule for locally differentially private data.
The Sentiments Analysis of Donald Trump and Jokowi’s Twitters on Covid-19 Policy Dissemination

As Covid-19 spreads to other nations and governments attempt to minimize its effect by introducing countermeasures, individuals have often used social media outlets to share their opinions on the measures themselves, the leaders implementing them, and the ways in which their lives are shifting. Sentiment analysis refers to the application in source materials of natural language processing, computational linguistics, and text analytics to identify and classify subjective opinions. The reason why this research uses a sentiment case study towards Trump and Jokowi's policies is because Jokowi and Trump have similarities in handling Covid-19. Indonesia and the US are still low in the discipline in implementing health protocols. The data collection period was chosen on September 21 - October 21 2020 because during that period, the top 5 trending on Twitter included # covid19, #jokowi, #miglobal, #trump, and #donaldtrump. So, this period is most appropriate for taking data and discussing the handling of Covid-19 by Jokowi and Trump. The result shows both Jokowi and Trump have higher negative sentiments than positive sentiments during the period. Trump had issued a controversial statement regarding the handling of Covid-19. This research is limited to the sentiment generated by the policies conveyed by the US and Indonesian Governments via @jokowi and @realDonaldTrump Twitter Account. The dataset presented in this research is being collected and analyzed using the Brand24, a software-automated sentiment analysis. Further research can increase the scope of the data and increase the timeframe for data collection and develop tools for analyzing sentiment.
Badminton Video Analysis Based on Player Tracking and Pose Trajectory Estimation

The traditional attitude estimation method is still not ideal for occlusion, which often ignores the continuity of human posture itself and the spatiotemporal continuity of human target trajectory. Therefore, this paper considers using the improved CAMSHIFT algorithm to track the athletes, taking badminton as an example, proposes a human posture estimation algorithm based on video. The scheme uses H, S, V to establish the athletes’ color model. The traditional histogram is replaced by weighted histogram and instance histogram. The algorithm also estimates the pose of the human body in static image, and establishes the human body tracking based on the measurement of the pose distance between frames. It generates better candidate keys for the key points with lower confidence in each frame, and generates the optimal global pose for each frame by recombining these body parts. Finally, the real-time semantic analysis scheme of badminton video is formed by using the above methods, and the performance indexes of the whole system are comprehensively analyzed through experiments. It ensures the tracking processing speed and reduces the error caused by similar tracking targets, which proves the feasibility and effectiveness