Reviewerly/exHarmony huggingface dataset:
Measurement-device-independent quantum key distribution protocols against collective noise

In order to eliminate the influence of the channel noise, two new measurement-device-independent quantum key distribution (MDI-QKD) protocols are proposed with logical quantum states. They can resist collective-dephasing noise and collective-rotation noise, respectively. This paper produces logical quantum states by adding the auxiliary light sources, the CNOT operations and the Hadamard transforms in the system model. The main light sources and auxiliary light sources are flexible and easily implemented, since they can be weak coherent state (WCS) sources, heralded single-photon sources (HSPSs) or heralded pair coherent state (HPCS) sources. To generate one key bit, the new MDI-QKD protocols only need one logical qubit with two particles so that they have high qubit efficiency. Moreover, the new protocols also use partial Bell-state measurement (BSM) which is very easily implemented with existing technologies.
Two-Dimensional Quantum Genetic Algorithm: Application to Task Allocation Problem

This paper presents a Two-Dimensional Quantum Genetic Algorithm (2D-QGA), which is a new variety of QGA. This variety will allow the user to take the advantages of quantum computation while solving the problems which are suitable for two-dimensional (2D) representation or can be represented in tabular form. The performance of 2D-QGA is compared to two-dimensional GA (2D-GA), which is used to solve two-dimensional problems as well. The comparison study is performed by applying both the algorithm to the task allocation problem. The performance of 2D-QGA is better than 2D-GA while comparing execution time, convergence iteration, minimum cost generated, and population size.
Augmented Reality Based Learning Environment for Children with Special Needs

Technology interference has become noticeable in all aspects of life. One of the most popular technologies is Augmented Reality (AR) used in all fields to facilitate and enhance those fields. The education filed is one of the areas in which augmented reality has been used extensively during the last years. Augmented reality has contributed to improving traditional educational methods through the integration of digital information with the user's environment in real-time. Therefore, augmented reality technology can be utilized to teach children with special needs due to its advantages in enriching the learning methodology. This paper introduces the design, development and evaluation of an efficient and low-cost AR-based educational environment for children with special needs. This environment utilizes augmented reality and educational cards to teach children the word from the card, its 3D model and its pronunciation. The proposed educational environment is developed as a mobile-based augmented reality (AR) application to be more attractive for children. The results showed that children enjoy sessions and interact with the application, which improves learning outcomes. Moreover, the user experience questionnaire shows a high level of satisfaction among parents and specialists.
Mathematics Teachers Standing on the Utilization of Digital Resources in Kathmandu, Nepal

The standing of mathematics teachers on the use of digital resources represents the mathematics teaching-related of online resources, mobile applications, and software. The study focused on how mathematics teachers are using digital resources in their classroom practices. The study was carried out among 147 mathematics teachers in Kathmandu Nepal. The finding indicates that digital resources using the level of mathematics teachers were found low. The teaching experience is a contributing factor to the use of online resources whereas teaching level and qualifications are contributing factors of using the software. The finding suggested all stakeholders bargain away out towards the regular and effective application of digital resources in the classroom practice and digital capacity enhancement of teachers.
Research on Personalized Learning Based on Collaborative Filtering Method

Abstract With the popularization of the Internet, the rapid development of intelligent education is promoted. They are a new platform for students to learn, and they can quickly help students improve and consolidate knowledge, as well as unlimited time, place, and space. However, due to a large number of learning resources, how to find the resources you need, and suitable from the massive resources is a problem that needs to be solved in this field. The individual characteristics of each learner are different, so when meeting their own needs, they have different requirements for individualization. The personalized learning resource recommendation system was born, according to the different characteristics of students to recommend corresponding learning resources. Since most of the recommendation systems currently recommend a large number of test questions or a large number of books, but each person’s needs are different, this article focuses on the above problems and selects a small piece of content course knowledge points in the learning resources, using course knowledge Come as a recommendation point and solve the above problems by designing a personal personalized learning mechanism. The main research contents of this article are as follows: (1) It is proposed to take curriculum knowledge points as the recommended objects. (2) Use collaborative filtering methods and cognitive diagnosis methods to design individual learning mechanisms. (3) Experiment. Through related experiments, the use of collaborative filtering method and cognitive diagnosis method based on curriculum knowledge points recommendation to confirm whether it is feasible.
Hybrid Density- and Partition-Based Clustering Algorithm for Data With Mixed-Type Variables

Clustering is an essential technique for discovering patterns in data.Many clustering algorithms have been developed to tackle the ever increasing quantity and complexity of data, yet algorithms that can cluster data with mixed variables (continuous and categorical) remain limited despite the abundance of mixed-type data.Of the existing clustering methods for mixed data types, some posit unverifiable distributional assumptions or rest on unbalanced contributions of different variable types.To address these issues, we propose a two-step hybrid density-and partitionbased (HyDaP) algorithm to detect clusters after variable selection.The first step involves both density-based and partition-based algorithms to identify the data structure formed by continuous variables and determine important variables (both continuous and categorical) for clustering.The second step involves a partition-based algorithm together with our proposed novel dissimilarity measure to obtain clustering results.Simulations across various scenarios were conducted to compare the HyDaP algorithm with other commonly used methods.Our HyDaP algorithm was applied to identify sepsis phenotypes and yielded important results.
The Ecosystem of Digital Content Governance

This article aims to explore the current complexity of the global process of online content moderation. The multiple actors with different roles and interests in content moderation form a complex network of government institutions, civil society organizations, and private companies that constitute an ecosystem. The article's focus is not only on content moderation practices and methods but also on exploring challenges and opportunities for improving governance aspects for the ecosystem of digital content moderation.
Transformations of Magistracy in Russia

The system of master education in Russia in now being transforming. It is influenced by the global trends in higher education and stakeholders’ needs. The goal of this research is to find out the substantial transformations in Russian magistracy and to underline the main trends at the current stage. To achieve the goal, the relevant literature and regulations were reviewed. Besides, we used the results of all-Russian survey “Russian Master’s Early Growth” devoted to Russian magistracy, the winner of the program “Scholarship program by Vladimir Potanin”. The research gives the analyses of transformations of magistracy in Russia in three vectors: changes in educational paths, staffing and design of master programs. The results of the research can be used by university management while designing master’s programs.
The Impact of Job Satisfaction on Organizational Citizenship Behavior: A Review of Literature

Employee job satisfaction is the balance between employees expectation from the organization and the rewards, both financial and non-financial, that an organization provides to employees.Organizational citizenship behavior is the extra contribution of the employee to the organization which is beyond the required work level as expected by the organization.This research paper aims to explore the effect of job satisfaction on organizational citizenship based on the literature review of selected research articles.It had been previously found that job satisfaction had a positive significant relationship with the organization citizenship behavior of employees in various other sectors and industries.The findings of this study confirmed that there is a significant and positive relationship exists between job satisfaction and organizational citizenship behavior of employees in different industries.Therefore it is recommended maintaining accepted level of job satisfaction among employees to enhance the organizational citizenship behavior.
Intermediate Loss Regularization for CTC-based Speech Recognition

We present a simple and efficient auxiliary loss function for automatic speech recognition (ASR) based on the connectionist temporal classification (CTC) objective. The proposed objective, an intermediate CTC loss, is attached to an intermediate layer in the CTC encoder network. This intermediate CTC loss well regularizes CTC training and improves the performance requiring only small modification of the code and small and no overhead during training and inference, respectively. In addition, we propose to combine this intermediate CTC loss with stochastic depth training, and apply this combination to a recently proposed Conformer network. We evaluate the proposed method on various corpora, reaching word error rate (WER) 9.9% on the WSJ corpus and character error rate (CER) 5.2% on the AISHELL-1 corpus respectively, based on CTC greedy search without a language model. Especially, the AISHELL-1 task is comparable to other state-of-the-art ASR systems based on auto-regressive decoder with beam search.
A Hybrid Recommendation System Based on Bidirectional Encoder Representations

Using auxiliary data about items provides more accurate item recommendations when utilizing deep learning in the recommendation system. Users often read item descriptions during online shopping, which contain key information about the item and its features. However the item descriptions are in unstructured form and using them in the deep learning model is a problem. In this study, we integrate a pioneering Natural Language Processing technique into a recommendation system to create an item embedding vector from unstructured item description text. The experimental results show that the proposed approach is efficient in generating more accurate recommendations by creating item embedding vectors from unstructured item description text.
Network Support for High-Performance Distributed Machine Learning

The traditional approach to distributed machine learning is to adapt learning algorithms to the network, e.g., reducing updates to curb overhead. Networks based on intelligent edge, instead, make it possible to follow the opposite approach, i.e., to define the logical network topology around the learning task to perform, so as to meet the desired learning performance. In this paper, we propose a system model that captures such aspects in the context of supervised machine learning, accounting for both learning nodes (that perform computations) and information nodes (that provide data). We then formulate the problem of selecting (i) which learning and information nodes should cooperate to complete the learning task, and (ii) the number of epochs to run, in order to minimize the learning cost while meeting the target prediction error and execution time. After proving important properties of the above problem, we devise an algorithm, named DoubleClimb, that can find a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$1+1/| \mathcal {I}|$ </tex-math></inline-formula> -competitive solution (with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathcal {I}$ </tex-math></inline-formula> being the set of information nodes), with cubic worst-case complexity. Our performance evaluation, leveraging a real-world network topology and considering both classification and regression tasks, also shows that DoubleClimb closely matches the optimum, outperforming state-of-the-art alternatives.
How Secure Are Two-Way Ping-Pong and LM05 QKD Protocols under a Man-in-the-Middle Attack?

We consider a man-in-the-middle attack on two-way quantum key distribution ping-pong and LM05 protocols in which an eavesdropper copies all messages in the message mode, while being undetectable in the mode. Under the attack there is therefore no disturbance in the message mode and the mutual information between the sender and the receiver is always constant and equal to one and messages copied by the eavesdropper are always genuine. An attack can only be detected in the control mode but the level of detection at which the protocol should be aborted is not defined. We examine steps of the protocol to evaluate its security and find that the protocol should be redesigned. We also compare it with the security of a one-way asymmetric BB84-like protocol in which one basis serves as the message mode and the other as the control mode but which does have the level of detection at which the protocol should be aborted defined.
FairBatch: Batch Selection for Model Fairness

Training a fair machine learning model is essential to prevent demographic disparity. Existing techniques for improving model fairness require broad changes in either data preprocessing or model training, rendering themselves difficult-to-adopt for potentially already complex machine learning systems. We address this problem via the lens of bilevel optimization. While keeping the standard training algorithm as an inner optimizer, we incorporate an outer optimizer so as to equip the inner problem with an additional functionality: Adaptively selecting minibatch sizes for the purpose of improving model fairness. Our batch selection algorithm, which we call FairBatch, implements this optimization and supports prominent fairness measures: equal opportunity, equalized odds, and demographic parity. FairBatch comes with a significant implementation benefit -- it does not require any modification to data preprocessing or model training. For instance, a single-line change of PyTorch code for replacing batch selection part of model training suffices to employ FairBatch. Our experiments conducted both on synthetic and benchmark real data demonstrate that FairBatch can provide such functionalities while achieving comparable (or even greater) performances against the state of the arts. Furthermore, FairBatch can readily improve fairness of any pre-trained model simply via fine-tuning. It is also compatible with existing batch selection techniques intended for different purposes, such as faster convergence, thus gracefully achieving multiple purposes.
Multi-model Ensemble to Classify Acute Lymphoblastic Leukemia in Blood Smear Images

Acute Lymphoblastic Leukemia (ALL) is one of the most commonly occurring type of leukemia which poses a serious threat to life. It severely affects White Blood Cells (WBCs) of the human body that fight against any kind of infection or disease. Since, there are no evident morphological changes and the signs are pretty similar to other disorders, it becomes difficult to detect leukemia. Manual diagnosis of leukemia is time-consuming and is even susceptible to errors. Thus, in this paper, computer assisted diagnosis method has been implemented to detect leukemia using deep learning models. Three models namely, VGG11, ResNet18 and ShufflenetV2 have been trained and fine tuned on ISBI 2019 C-NMC dataset. Finally an ensemble using weighted averaging technique is formed and evaluated as per the criteria of binary classification. The proposed method gave an overall accuracy of 87.52% and F1-score of 87.40%. Thus, it outperforms most of the existing techniques for the same dataset.
Research on the Application of Computer Technology in Innovation and Entrepreneurship Education

Abstract With the promotion of mass entrepreneurship and innovation strategy, University Innovation and Entrepreneurship (hereinafter referred to as IAE) has become an important research topic, which will improve the IAE ability and management level of college students. Computer technology has been used in medical practice, military training, entertainment games and other fields. In the field of education, computer technology has a wider application and influence, such as VR technology. VR technology application can meet the requirements of situational and interactive, which can be applied to IAE education. Based on the characteristics of College Students’ IAE development, this paper develops a system suitable for college students’ IAE project management, which will be better applied to the application of College Students’ IAE education. First of all, this paper puts forward the necessity of the application of computer technology in IAE education. Then, this paper designs the related construction requirements. Finally, some suggestions are put forward.
Optimal covariant quantum measurements

We discuss symmetric quantum measurements and the associated covariant observables modelled, respectively, as instruments and positive-operator-valued measures. The emphasis of this work are the optimality properties of the measurements, namely, extremality, informational completeness, and the rank-1 property which contrast the complementary class of (rank-1) projection-valued measures. The first half of this work concentrates solely on finite-outcome measurements symmetric w.r.t. finite groups where we derive exhaustive characterizations for the pointwise Kraus-operators of covariant instruments and necessary and sufficient extremality conditions using these Kraus-operators. We motivate the use of covariance methods by showing that observables covariant with respect to symmetric groups contain a family of representatives from both of the complementary optimality classes of observables and show that even a slight deviation from a rank-1 projection-valued measure can yield an extreme informationally complete rank-1 observable. The latter half of this work derives similar results for continuous measurements in (possibly) infinite dimensions. As an example we study covariant phase space instruments, their structure, and extremality properties.
Research on Convolutional Neural Network Image Recognition Algorithm Based on Computer Big Data

Abstract Aiming at the problems of slow convergence and low recognition accuracy of the existing image big data recognition methods, a convolution neural network based image big data recognition method is proposed. In the concept of machine vision, image recognition refers to the ability of software to distinguish characters, positions, objects, actions and handwriting in pictures. Computers can apply machine vision techniques, combined with artificial intelligence and cameras to image recognition. Describes the convolution layer, pool layer, full connection layer and classification layer in the model. MNIST data sets are simulated to verify the superiority of the proposed method. Convolutional neural network has achieved good results in the field of image recognition, but its network structure has a great impact on the effect and efficiency of image recognition. In order to improve the recognition performance, a new convolution neural network structure is designed and implemented by repeated use of smaller convolution kernels. The results show that compared with other similar algorithms, the false recognition rate of this method is low, and the lower average square error of classification can be realized in a short time. This paper expounds the basic flow of digital recognition system from the aspects of data extraction and preprocessing, feature extraction and selection, classifier design and so on [1] .
METHODOLOGICAL MODEL OF APPLYING AUGMENTED REALITY AT READING LESSONS IN PRIMARY SCHOOL

The article analyses the features of publications of works of art by Ukrainian and foreign writers accompanied by AR applications. The authors substantiate the relevance and outlined the prospects for the use of augmented reality books in reading lessons in primary school. The article demonstrates the possibilities of using AR-technology for the development of creative imagination, emotional intelligence, creative and figurative thinking. A methodical model of augmented reality application at Reading lessons in primary school has been developed. The main stages of this model are detailed: involvement; interaction; listening, reading and listening; research; creative work; evaluation. The main purpose of the proposed model is to involve the “digital generation” in reading activities, the formation of primary school students’ need to learn about the world through fiction. Theauthorshave developedanumber ofgametasks, duringwhichjuniorstudentshave theopportunity to explore the virtual world of the work of art in detail, as well as to interact with literary characters. This approach allows students to visually imagine the reality depicted by the writer, to approach the understanding of images, to learn more about the values, being in the virtual surface of the art world, which contributes to the creative imagination of students and the formation of their figurative thinking. The creative stage involves students expressing their own attitude to what is read. Students are invited to think, compose simple texts about their thoughts, impressions, observations. Besides, on the basis of a work of art, students learn to fantasise: change the ending of the story or invent a new story with the hero of the work of art. The results of reading activities of junior students in the classroom with the use of augmented reality is proposed to evaluate the following criteria: motivational, emotional, active, and valuable. According to these criteria, the indicators are outlined. The results of the study, a survey of primary school teachers and students prove the effectiveness of the use of AR-applications and their benefits for enhancing the reading activities of students.
Review on Deep Learning Handwritten Digit Recognition using Convolutional Neural Network

In this digital world, everything including documents, notes is kept in digital form. The requirement of converting these digital documents into processed information is in demand. This process is called as Handwritten digit recognition (HDR). The digital scan document is processed and classified to identify the hand written words into digital text so that it can be used to keep it in the documents format means in computerized font so that everybody can read it properly. In this paper, it is discussed that classifiers like KNN, SVM, CNN are used for HDR. These classifiers are trained with some predefined dataset and then used to process any digital scan document into computer document format. The scanned document is passed through four different stages for recognition where image is pre-processed, segmented and then recognized by classifier. MNIST dataset is used for training purpose. Complete CNN classifier is discussed in this paper. It is found that CNN is very accurate for HDR but still there is a scope to improve the performance in terms of accuracy, complexity and timing.