Reviewerly/exHarmony huggingface dataset:
Co-Learning as a New Model of Learning in a Digital Environment: Learning Effectiveness and Collaboration

During the last decade, co-learning techniques have been widely used for the formation of an effective open society. Co-learning allows increasing the skills of social adaptation and joint work of representatives of inhomogeneous groups. The purpose of this study was to determine educational effectiveness, as well as danger, the effectiveness of the digital environment in developing social skills and achieving learning quality in a new generation. The study was conducted in the form of a survey among 60 students from Gulin Tourism University and Mos-cow State Pedagogical University; they were divided into two groups, the meth-ods of cooperative and collaborative co-education with immersion in a digital en-vironment were used. A control group consisted of at least 30 students. The real effectiveness of training was tested both by the solution of task and by independ-ent testing that was obtained in the process of solving. A separate survey was conducted to establish a subjective assessment by students of the social skills and interaction. The survey was conducted according to the methods that have been developed over the past 6 years by different groups of researchers. Consequently, it was found out that co-learning technologies do not influence the quality of training and the assimilation, and students immersed in a digital environment showed even better results. The number of students with most correct answers for the test was 46.67% and 23.33% in study groups, and only 10% – in control group. Generally, the results of a subjective assessment of social skills coincides with similar data obtained by other researchers and shows an improvement of collaboration in inhomogeneous groups, slighter leadership in common problems solving, and very low conflict indicators. Generally, the results of a subjective as-sessment of social skills coincides with similar data obtained by other researchers and shows an improvement of collaboration in inhomogeneous groups, slighter leadership in common problems solving, and very low conflict indicators.
A non-parametric test to detect data-copying in generative models

Detecting overfitting in generative models is an important challenge in machine learning. In this work, we formalize a form of overfitting that we call {\em{data-copying}} -- where the generative model memorizes and outputs training samples or small variations thereof. We provide a three sample non-parametric test for detecting data-copying that uses the training set, a separate sample from the target distribution, and a generated sample from the model, and study the performance of our test on several canonical models and datasets. For code \& examples, visit this https URL
Research on Teaching Quality Evaluation Method of Network Course Based on Intelligent Learning

In view of the problems of poor evaluation effect and high evaluation delay in the existing evaluation methods for network course teaching quality, this paper designs an evaluation method based on intelligent learning. The design idea is as follows: with the help of public services, different forms of teaching tasks are summarised and data reduction is processed. On this basis, the restricted Boltzmann machine method based on intelligent learning is adopted to design the evaluation algorithm, and the teaching quality is evaluated with weighting parameters and preference parameters. Through the application part, different application functions such as information query, scene construction, data management and view viewing during the network course teaching are realised. The experimental results show that this method has high evaluation efficiency, short evaluation delay, and significantly reduced actual use of class time, which guarantees the teaching quality of teachers and the overall learning effect of students, and has high application advantages.
RETRACTED: Application of English education information management system based on convolution neural network classification algorithm

In order to improve the convenience of English education management and improve the quality of teaching, this paper proposes the application of English education information management system based on convolution neural network classification algorithm. Specifically, it relies on spark cloud technology and data mining technology of cash to build an English education information management system. Secondly, in the educational information management platform, convolution neural network classification algorithm is used to mine useful information. Convolution network mainly combines the emotional words, parts of speech, degree adverbs, negatives, punctuation and other word features that affect the emotional tendency of the text to form an extended text feature. Then, the word vector feature and the extended text feature are used as two input channels of convolution neural network, and the dynamic K-max pooling strategy is adopted to improve the ability of feature extraction of the model. The experiment in the English education information management system shows that the classification performance of the algorithm in this paper is not only higher than that of the single channel convolution neural network algorithm, but also has some advantages compared with some representative algorithms. In addition, experiments verify the effectiveness of the educational information management system.
Nonclassicality and entanglement properties of non-Gaussian entangled states via a superposition of number-conserving operations

We theoretically investigate the nonclassicality and entanglement properties of non-Gaussian entangled states generated by using a number-conserving generalized superposition of products (GSP), i.e., $$\left( saa^{\dag }+ta^{\dag }a\right) ^{m}$$ with $$s^{2}+t^{2}=1$$ on each mode of an input two-mode squeezed coherent (TMSC) state. The simulation results show that, compared to the typical two-mode squeezed vacuum state, the usage of small coherent amplitude is conductive to offering an opportunity for not only effectively enhancing the nonclassicality in terms of antibunching effect and Wigner function, but also significantly improving the entanglement quantified by Einstein–Podolsky–Rosen correlation and Hillery–Zubairy correlation. For the increase of the number of operations, the region of both the existing antibunching effect and the improved entanglement decreases, but this region of the improved teleportation fidelity and the negative distribution of the Wigner function is on the increase. Under an ideal Braunstein and Kimble teleportation protocol, when the generated states are treated as an entangled resource, the optimal teleportation fidelity can be achieved by taking a suitable squeezing parameter and the number of operations for the optimal choices of s. In order to highlight the advantages of the use of the GSP-embedded TMSC, under the same parameters, we also make a comparison about the performances of both the entanglement and the fidelity for different non-Gaussian entangled states, involving the photon-subtracted-then-added TMSC states and the photon-added-then-subtracted TMSC states. It is found that in the regime of small squeezing values, both of the entanglement and the fidelity for the generated states can perform better than the other cases.
Block-ED: The Proposed Blockchain Solution for Effectively Utilising Educational Resources

Abstract In this day and age, access to the Internet has become very easy, thereby providing access to different educational resources posted on the cloud even easier. Open access to resources, such as research journals, publications, articles in periodicals etc. is restricted to retain their authenticity and integrity, as well as to track and record their usage in the form of citations. This gives the author of the resource his fair share of credibility in the community, but this may not be the case with open educational resources such as lecture notes, presentations, test papers, reports etc. that are produced and used internally within an organisation or multiple organisations. This calls for the need to build a system that stores a permanent and immutable repository of these resources in addition to keeping a track record of who utilises them. Keeping in view the above-mentioned problem in mind, the present research attempts to explore how a Blockchain based system called Block-ED can be used to help the educational community manage their resources in a way to avoid any unauthorised manipulations or alterations to the documents, as well as recognise how this system can provide an innovative method of giving credibility to the creator of the resource whenever it is utilised.
Latent <i>D</i>-Scoring Modeling: Estimation of Item and Person Parameters

This study presents a latent (item response theory–like) framework of a recently developed classical approach to test scoring, equating, and item analysis, referred to as D-scoring method. Specifically, (a) person and item parameters are estimated under an item response function model on the D-scale (from 0 to 1) using marginal maximum-likelihood estimation and (b) analytic expressions are provided for item information function, test information function, and standard error of estimation for D-scores obtained under the proposed latent treatment of the D-scoring method. The results from a simulation study reveal very good recovery of item and person parameters via the marginal maximum-likelihood estimation method. Discussion and recommendations for practice are provided.
A rapid white blood cell classification system based on multimode imaging technology

Abstract In order to simplify the complexity of white blood cell classification in existing point‐of‐care testing (POCT) testing equipment, a white blood cell classification detection system based on microfluidic and multimode imaging was constructed. Microfluidic chip was used in the system. A multimodal optical imaging system based on the characteristics of blood samples was designed to obtain eigenvalue extraction of cells. Afterward, a BP neural network model was constructed to realize automatic classification of white blood cells. Finally, 80 human blood samples were classified and detected by this system and compared with the results of Sysmex XE‐5000. The consistency correlation coefficients of white blood cells, lymphocytes, monocytes, neutrophils and eosinophils are 1.038, 0.907, 0.549, 0.922 and 1.028, respectively, and the CV values of the four types of white blood cells in the stability test were all below 10%. In this study, a white blood cell classification and detection system with small size, simple operation, fast single‐sample detection, high accuracy, and no maintenance is required. It will provide a solid technical support for the further development of POCT blood cell analysis equipment.
CID: Categorical Influencer Detection on microtext-based social media

Purpose This paper proposes an approach to identify categorical influencers (i.e. influencers is the person who is active in the targeted categories) in social media channels. Categorical influencers are important for media marketing but to automatically detect them remains a challenge. Design/methodology/approach We deployed the emerging deep learning approaches. Precisely, we used word embedding to encode semantic information of words occurring in the common microtext of social media and used variational autoencoder (VAE) to approximate the topic modeling process, through which the active categories of influencers are automatically detected. We developed a system known as Categorical Influencer Detection (CID) to realize those ideas. Findings The approach of using VAE to simulate the Latent Dirichlet Allocation (LDA) process can effectively handle the task of topic modeling on the vast dataset of microtext on social media channels. Research limitations/implications This work has two major contributions. The first one is the detection of topics on microtexts using deep learning approach. The second is the identification of categorical influencers in social media. Practical implications This work can help brands to do digital marketing on social media effectively by approaching appropriate influencers. A real case study is given to illustrate it. Originality/value In this paper, we discuss an approach to automatically identify the active categories of influencers by performing topic detection from the microtext related to the influencers in social media channels. To do so, we use deep learning to approximate the topic modeling process of the conventional approaches (such as LDA).
Using Blockchain Technology for Continuing Medical Education Credits System

In recent years, the rapid development of Blockchain Technology has found its applications in sectors like Finance, Healthcare, Supply Chain Management, Internet of Things (IoT), and many more. In this paper, we propose use of Blockchain to the system of accreditation of Continuing Medical Evaluation (CME) credits and a physician's Medical Licence Renewal. To be able to practice medicine, a physician needs to possess a legitimate licence approved by a verified Medical Council and for this, certain credit hours need to be earned. CME is a global education system that aims to continually upgrade the knowledge of physicians with the advancements made in the medical domain. By including the highly robust features of blockchain like security, decentralization, and immutability in the existing system of CME Credits, many of the extant problems can be uprooted and a system can be established. This work aims at providing an Ethereum-based solution to enhance the existing CME Credits system by providing an automated credits accreditation and medical licence renewal system.
Stylized Adversarial Defense

Deep Convolution Neural Networks (CNNs) can easily be fooled by subtle, imperceptible changes to the input images. To address this vulnerability, adversarial training creates perturbation patterns and includes them in the training set to robustify the model. In contrast to existing adversarial training methods that only use class-boundary information (e.g., using a cross-entropy loss), we propose to exploit additional information from the feature space to craft stronger adversaries that are in turn used to learn a robust model. Specifically, we use the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">style</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">content</i> information of the target sample from another class, alongside its class-boundary information to create adversarial perturbations. We apply our proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">multi-task</i> objective in a deeply supervised manner, extracting multi-scale feature knowledge to create maximally separating adversaries. Subsequently, we propose a max-margin adversarial training approach that minimizes the distance between source image and its adversary and maximizes the distance between the adversary and the target image. Our adversarial training approach demonstrates strong robustness compared to state-of-the-art defenses, generalizes well to naturally occurring corruptions and data distributional shifts, and retains the model's accuracy on clean examples.
Pre-trained Language Embedding-based Contextual Summary and Multi-scale Transmission Network for Aspect Extraction

With the development of IOT and 5G technology, people’s demand for information acquisition is more inclined to accuracy, intelligence and timeliness. How to help designer obtain the real-time information of specific product reviews from the massive online consumers and upgrade the new design strategy has become a hot topic for research. In this paper, we define the problem as an aspect extraction task, and propose a novel deep learning model that comprises of three modules: pre-training language model embedding, multi-scale transmission network and contextual summary, which aims to provide an end-to-end solution without any additional supervision. To this end, we adopt BERT to overcome the disadvantage of traditional embedding methods, which cannot combine contextual information. Multi-scale transmission network is proposed to integrate the Bi-GRU and a group of CNN networks to extract sequential and local features of words respectively. Contextual summary is a tailor-made representation distilled from the input sentence, conditioned on each current word, and thus can assist aspect prediction. Experimental results over three benchmark SemEval datasets clearly illustrate that our model can achieve the state-of-the-art performance.
A machine learning approach for gender identification of Greek tweet authors

Digital communities and social media are widely used and produce a huge amount of information every second. Text analysis has been widely used by researchers and machine learning (ML) engineers for automating the author profiling task. Author profiling can be used in marketing and business intelligence frameworks but also remains a strong factor in crime investigations gaining more insight regarding the suspect. In this paper we describe the process we used in obtaining a new Twitter corpus and propose an ML approach to determine the gender of Greek author's tweets. The best result (0.7 accuracy) was obtained using SVMs and TF-IDF encoding.
A Lean Convolutional Neural Network for Vehicle Classification

Image classification is an important task in machine vision, in which vehicle classification is used for different applications like traffic analysis, autonomous driving, security, among others. Recent studies made with Convolutional Neural Networks (CNN) have shown that these networks have surpassed older algorithms like Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) in terms of accuracy, speed, and resources management. Even though that CNN have better accuracy and speed they still are heavy in resource consumption on computers which makes them not suitable to deploy on an embedded platform. This paper proposes a lean CNN that has a smaller number of parameters and still maintaining the best accuracy possible on vehicle classification.
Pushing LIMITS

As a pandemic rages and ecosystems around the globe collapse, the LIMITS community---along with the rest of the world---is working to adapt. Some adaptations to adjust to emergencies are easy to imagine. For example, months before in-person conferences were canceled in response to COVID-19, the User Interface Software and Technology (UIST) 2019 conference took significant steps towards hosting a geographically distributed, virtual conference, in part adapting to the global climate emergency. This type of change is conceptually clear, if organizationally challenging. However, many needed changes are conceptually difficult, even in the midst of an existential crisis. It is long-recognized that we need to bridge the separation between scholarly venues and publications that focus on technical aspects of computing systems (i.e., "applied") and those that center social and political aspects of computing systems research and design, particularly when attempting to address complex life-wide problems. Yet, disciplinary crystals (e.g., siloes) remain resistant to change. The authors of this paper contribute to ongoing socio-technical efforts, identifying dominant practices and forces that reinforce the socio-technical divide, and holding up empirical projects that offer promising alternatives.
LD-Net: An Efficient Lightweight Denoising Model Based on Convolutional Neural Network

The removal of impulse noise is a crucial pre-processing step in image processing systems. In recent years, numerous noise-removal methods have been proposed to improve denoizing performance and reconstruct noise-free images. However, removing high-density impulse noise remains a major challenge. In this paper, to address the image denoizing problem associated with high-density noise, we propose a new denoizing model, called LD-Net, which can be trained end-to-end and directly reconstructs noise-free images via a lightweight convolutional neural network. LD-Net is performed in two stages including a feature augmentation stage and a feature refinement stage. During the feature augmentation stage, the spatial size and dimension of the input image are increased by employing the deconvolutional layers for effective feature learning. During the feature refinement stage, the textural details of the image are enhanced for the reconstruction of the noise-free image by the utilization of a proposed sequence of three convolutional layers. Quantitative and qualitative evaluations performed on the SN-LABELME dataset indicate that the proposed LD-Net removes high-density impulse noise more effectively and at higher speed than other state-of-the-art denoizing methods.
Dynamic pose estimation of uncooperative space targets based on monocular vision

A novel algorithm of dynamic pose estimation for monocular visual sensor is proposed in this paper. The sensor is principally composed of two 1D turntables, one collimated laser, and one industrial camera. In particular, the proposed algorithm is suitable for the cases of uncooperative targets. By analyzing the motion of a laser beam based on quaternion, the functional detection algorithm is derived from the position information of multiple scanning points. Furthermore, the depth recovery based on a nonparametric model is a key step in the pose calculation, which is unnecessary to make use of the calibration parameters of an industrial camera. It is, however, effective to avoid the influence of camera distortion and calibration error. After establishing a test platform, simulation and experiments for pose estimation are carried out. The experimental results show that the maximum error is 0.98° at a range of 500 mm, which proves that the proposed algorithm is accurate and effective.
Subgoaling Techniques for Satisficing and Optimal Numeric Planning

This paper studies novel subgoaling relaxations for automated planning with propositional and numeric state variables. Subgoaling relaxations address one source of complexity of the planning problem: the requirement to satisfy conditions simultaneously. The core idea is to relax this requirement by recursively decomposing conditions into atomic subgoals that are considered in isolation. Such relaxations are typically used for pruning, or as the basis for computing admissible or inadmissible heuristic estimates to guide optimal or satis_cing heuristic search planners. In the last decade or so, the subgoaling principle has underpinned the design of an abundance of relaxation-based heuristics whose formulations have greatly extended the reach of classical planning. This paper extends subgoaling relaxations to support numeric state variables and numeric conditions. We provide both theoretical and practical results, with the aim of reaching a good trade-o_ between accuracy and computation costs within a heuristic state-space search planner. Our experimental results validate the theoretical assumptions, and indicate that subgoaling substantially improves on the state of the art in optimal and satisficing numeric planning via forward state-space search.