Reviewerly/exHarmony huggingface dataset:
Personalized Learning Service Based on Big Data for Education

According to modern educational thoughts and theories, the paper uses the new information technology to study the personalized learning service based on big data for education and artificial intelligence technology, so as to provide learners with intelligent services of personalized learning. Under the technical support represented by big data, the paper builds dynamic and diverse learning resources, continuously collects and analyzes learner-related data, and provides differentiated teaching, personalized learning and accurate services in real time. It can meet the individual social and autonomous learning needs of each learner, match learning requirements intelligently and provide adaptive learning services. It intelligently builds a learning model suitable for each learner and realizes personalized learning in a real sense.
Construction and Implementation of Blended Teaching Ecology Based on Cloud Class

In order to effectively integrate online and offline teaching and improve teaching quality, according to relevant teaching theories, we take knowledge points as the core, from the three aspects of online learning environment, learning team and real-time feedback, explain the process of creating a blended teaching ecology based on cloud classroom, and take the “Network Security” course as an example, with Ding Talk + Zhejiang universities and colleges online open course sharing platform used as medium, introduce the whole teaching process before, during, and after class. This kind of blended teaching ecological teaching practice finally evaluates the teaching effect through quantitative analysis and qualitative analysis of experimental results.
Study on Evaluation of Chemical Industry Safety Production Based on Artificial Neural Network

Safety evaluation is to use relevant technical which evaluate the risks of the enterprise's production process. The article analyzes the shortcomings of the current chemical safety production evaluation system based on the artificial neural network knowledge. At the same time, a safety evaluation index system for chemical companies was established, and the advantages of applying neural networks in the evaluation system of chemical safety production were pointed out.
3D Virtual Content for Education Applications

In this paper we present the process of creating 3D models to develop augmented reality applications-oriented to the teaching-learning process. We present the process of creating three-dimensional models from conceptualization, polygonal modeling in 3ds max, creation of mapping coordinates in RizomUV, to the process of creating PBR materials in specialized soft-ware such as Substance Designer, Alchemist, and export and importation to Unity 3D. The developed models are incorporated into a virtual reality platform, which allows to exploring the flora and fauna of the Cotopaxi National Park, Latacunga, Ecuador.
Personality Traits, Speech and Adaptive In-Vehicle Voice Output

The interaction between human and machine is supposed to become more and more intuitive and natural. One prerequisite to achieve this is the ability of a spoken dialogue system to react flexibly to the individual requirements of a user, e.g., by means of adaptive voice output. In this context, a user's personality, which is directly reflected in his or her language style, represents a valuable framework to attribute linguistic differences in spoken language and design user-adaptive voice output. The need for such efficient, personalized communication becomes particularly safety-relevant in situations where speech-based interaction is required to be performed in parallel with a prioritized primary task, e.g., when driving a car. For this purpose, we performed data collection in a driving simulator and investigated user speech during the primary task of driving with a focus on the syntactic level. Our results revealed five syntactic complexity factors to be considered in the generation of voice output, which indicated significant differences in the spoken language of six different personality type clusters. This analysis serves as a basis for future work towards user and situation-adaptive voice output in dual-task environments.
Optimisation of archival processes involving digitisation of typewritten documents

Purpose The authors investigate optical character recognition (OCR) technology and discuss its implementation in the context of digitisation of archival materials. Design/methodology/approach The typewritten transcripts of the Croatian Writers' Society from the mid-60s of the 20th century are used as the test data. The optimal digitisation setup is investigated in order to obtain the best OCR results. This was done by using the sample of 123 pages digitised at different resolution settings and binarisation levels. Findings A series of tests showed that different settings produce significantly different results. The best OCR accuracy achieved at the test sample of the typewritten documents was 95.02%. The results show that the resolution is significantly more important than binarisation pre-processing procedure for achieving better OCR results. Originality/value Based on the research results, the authors give recommendations for achieving optimal digitisation process setup with the aim of increasing the quality of OCR results. Finally, the authors put the research results in the context of digitisation of cultural heritage in general and discuss further investigation possibilities.
Development and evaluation of an Urdu treebank (CLE-UTB) and a statistical parser

A number of natural language processing tools for Urdu language processing have been developed in the past few years for word segmentation, part of speech tagging, chunking, named entity recognition and parsing. Corpora, especially treebanks, are essential data resources for language processing. This work presents the development and evaluation of an Urdu treebank, the CLE-UTB and a statistical parser. The treebank has been annotated with phrase structure annotation. Part of speech tagging has been performed semi-automatically by using an existing tagger and incorrect tags were corrected manually by annotators. The syntactic annotation has been performed in the Penn Treebank style to mark phrases. The annotation scheme also adds functional labels for grammatical roles. Currently, the treebank contains 7854 annotated sentences and 148,575 tokens. Completeness and correctness of the syntactic labels have been checked automatically after manual annotation. To ensure the annotation consistency of the resource, a grammar-based evaluation and an automatic consistency checking tool have been used to detect linguistically implausible constituents. The inter-annotator agreement is greater than 90%. We have developed a bidirectional long-short term memory (BiLSTM) based parser and a POS tagger which have been trained on the final version of the treebank. We have improved our results by training the word embeddings on a large Urdu text corpus. Our parser produced an f-score of 88.1% and the POS tagger performed with an accuracy of 96.3%.
A quantum related-key attack based on the Bernstein–Vazirani algorithm

Due to the powerful computing capability of quantum computers, cryptographic researchers have applied quantum algorithms to cryptanalysis and obtained many interesting results in recent years. In this paper, we study related-key attack in the quantum setting and propose a specific related-key attack, which can recover the key of block ciphers efficiently as long as the attacked block ciphers satisfy certain condition. The attack algorithm employs the Bernstein–Vazirani algorithm as a subroutine and requires the attacker to query the encryption oracle with quantum superpositions. We give a condition under which the attack will succeed and prove that any block cipher either satisfies the condition or has a distinguishing attack. As a specific example of its application, we use the attack algorithm to extract the private key of the Even–Mansour cipher. The results of this study show the power of related-key attack when combined with quantum algorithms and provide guidance for the design of quantum-secure block ciphers.
Cultivation Strategy of College Students’ Craftsman Spirit from the Perspective of Artificial Intelligence

Abstract With the continuous deepening of artificial intelligence in education informatization, the transformation of education methods and learning methods under various new technologies has great prospects for promoting the research and application of personalized and deep learning in the AI environment. This article mainly studies the cultivation strategy of college students’ craftsman spirit in the perspective of artificial intelligence. Based on the current situation of digital literacy, achievement gaps, and outdated knowledge, this article explores new methods suitable for the new environment, such as role change, innovative education, integration of multiple learning methods, and creation of new learning spaces. The further development of higher education in the context of artificial intelligence provides exploration and discovery to serve the future of students. The experimental analysis of this article found that the correlation coefficients of the factors of the scale were between 0.394 and 0.618, which were basically at a moderate level and basically met the requirements of 0.10 to 0.60. At the same time, the correlation between the factors and the total score of the questionnaire was between 0.724 and 0.833. Meet the requirements. The cultivation of craftsman spirit meets the needs of colleges and universities to cultivate new talents, and provides theoretical support for colleges and universities to carry out spiritual and cultural education.
A novel deep quality-aware CNN for image edge smoothening

Image-edge smoothing is a practical technique in many image-editing implementations such as Visual Aesthetic Enhancement, Action Movie Production, and Privacy Protection. In the literature, there have been many well-adapted smoothing techniques used for image edges such as Gaussian and Bilateral Blurred utilizing visual semantics perceived by the human visual system. However, it is observed that the previously employed image-edge smoothing techniques are not both content-aware and semantically aware. In other words, the image edges are not smoothsimple and efficient, its performance might be less satisfactory. We tend to smooth semantically non-important regions while smoothing semantically important regions slightly in practice. The motivation behind this is that we propose a deep architecture for semantically aware image-edge smoothing. Hence, provided massive image patches extracted from highly aesthetic images crawled from Flickr, we extract multimodal visual features to characterize each. Then, these multimodal features are optimally combined via a multi-view learning framework. Hence, we employ an active learning framework to extract the visually attractive image patches from each image. Thus, we train a deep CNN to find out the edge attributes of actively selected image patches. Utilizing the trained CNN feature of each image patch, we construct a Gaussian mixture model to conduct image-edge smoothing. To verify the proposed method, extensive experimental runs on images with different styles are employed to present the attractiveness of image edges processed by the proposed method. Besides, the efficiency of the proposed method is compared to the well-known edge smoothing algorithms.
Improvement of the use of computer vision through the introduction of neural networks with clustering

Abstract The article touches upon the issues of the application of clustering technique and computer vision in order to identify additional parameters in image analysis. In addition to simple recognition of objects, it is possible to determine the specific components of the object of observation, for example, in humans it is: arms, hands and feet. Image clustering gives room for creating algorithms for the recognition of the actions of people. For example, if there is a fight between two people on the street, then using the obtained images from a video camera, it will be possible to inform the police that a fight is at a particular address or to identify potentially dangerous actions as well as an aggressor in an automated mode.
Hands on Data and Algorithmic Bias in Recommender Systems

This tutorial provides a common ground for both researchers and practitioners interested in data and algorithmic bias in recommender systems. Guided by real-world examples in various domains, we introduce problem space and concepts underlying bias investigation in recommendation. Then, we practically show two use cases, addressing biases that lead to disparate exposure of items based on their popularity and to systematically discriminate against a legally-protected class of users. Finally, we cover a range of techniques for evaluating and mitigating the impact of these biases on the recommended lists, including pre-, in-, and post-processing procedures. This tutorial is accompanied by Jupyter notebooks putting into practice core concepts in data from real-world platforms.
<i>U</i>-Statistical Inference for Hierarchical Clustering

Clustering methods are valuable tools for the identification of patterns in high-dimensional data with applications in many scientific fields. However, quantifying uncertainty in clustering is a challenging problem, particularly when dealing with high-dimension low sample size (HDLSS) data. We develop a U-statistics based clustering approach that assesses statistical significance in clustering and is specifically tailored to HDLSS scenarios. These nonparametric methods rely on very few assumptions about the data, and thus can be applied to a wide range of datasets for which the Euclidean distance captures relevant features. Our main result is the development of a hierarchical significance clustering method. To do so, we first introduce an extension of a relevant U-statistic and develop its asymptotic theory. Additionally, as a preliminary step, we propose a binary nonnested significance clustering method and show its optimality in terms of expected values. Our approach is tested through multiple simulations and found to have more statistical power than competing alternatives in all scenarios considered. Our methods are further showcased in three applications ranging from genetics to image recognition problems. Code for these methods is available in R-package uclust. Supplementary materials for this article are available online.
Data Sources and Data Tools: Preparing for the Open Data Ecosystem

This chapter offers an introduction to the basic concepts of strategically finding and evaluating publicly available data for health analysis. The wide array of skills needed by public health practitioners to manage and use data assets are described. Leading data providers and sources, at the local, state, and national levels, are introduced and reviewed as exemplars. In the evolving and dynamic universe of available health data, a variety of statistical tools and techniques as well as methods to organize complex work schemes are necessary for data acquisition, management, interpretation, and communication. The data ecosystem has matured from one-off availability of curated datasets to being organized by libraries of diverse data and the ability to analyze and visualize data in holistic data commons environments. Data sharing has been successful through legal and policy changes and is supported by the trust of appropriate attribution and the possibilities of innovative synergy across heterogenous domains.
Application of Mixed Reality Technology in Education with the case of a Huangmei Opera Cultural Education System

Mixed reality is an emerging technology that allows virtual world objects and real world objects to coexist and interact in real time. Its positioning lies between virtual reality and augmented reality, providing users with an immersive experience of combining virtual and real. In this article, we take the Huangmei Opera cultural educational system as an example, and use the current mainstream mixed reality hardware platform and its supporting content production softwares to implement interactive games and virtual museums about Huangmei Opera culture and knowledge. And to explore the application of mixed reality in education.
Progressive transduction nonnegative matrix factorization for dimensionality reduction

Dimensionality reduction is one important technique to find out meaningful representations of data by discovering the underlying structures of data. As one widely applied method, by learning parts-based representations, nonnegative matrix factorization (NMF) has been widely researched and used to various application fields. Compared with the previous methods, this paper presents a novel semi-supervised NMF learning framework, called progressive transduction NMF (PTNMF), that learns a robustly discriminative representation by introducing a progressive transduction structure. Specifically, a progressive transduction based scheme is employed to gradually update the representations of unlabeled points according to the similarity between the labeled and unlabeled data points. This is helpful to improve the effectiveness of NMF, especially for the case that labeled data is inadequate. The efficiency of the proposed method is discussed both theoretically and empirically. Extensive experiments on several real-world data sets are listed, and the experimental results demonstrate that the proposed algorithm obtains better discriminant ability in comparison to the state-of-the-art methods.
MAGAN: A masked autoencoder generative adversarial network for processing missing IoT sequence data

Missing sequence data prevent local data from reflecting the overall distribution of a sample, hindering data analysis. The problem of missing data during actual production is a serious issue and results in a high defect rate, low dimensionality, and high noise level. In this study, a Masked Generative Adversarial Network (MAGAN) model is proposed that is less affected by the data loss rate than a baseline comparison model, and at an 80% missing data rate, the model can still better reflect the distribution of real data. MAGAN shows better results than a traditional processing method for dealing with missing data.
ApproxDNN: Incentivizing DNN Approximation in Cloud

Service providers leverage discounted prices of reserved instances offered by cloud providers to amortize their operational costs. They reserve a certain number of instances to cover a significant portion of their computing resource requirements, and further employ on-demand instances to cover remaining requirements not satisfied by the reserved instances. Because of the higher price of on-demand instances, service providers seek to lower their usage to minimize operational costs. In this work, we propose ApproxDNN approach for Machine Learning as a Service to reduce operational costs of service providers by incentivizing approximate results, based on the capabilities of cutting-edge GPUs and a discounted pricing model. When the deadlines of jobs submitted by users are very tight, a service provider might not be able to execute all of them on reserved instances under the default precision. In such cases, Ap- proxDNN leverages the reduced-precision instructions to reduce the execution time of the jobs with slight reduction in their final accuracy, and consequently, to minimize the employment of on- demand instances. To incentivize users to accept the approximate results of reduced-precision instructions, ApproxDNN offers them a discounted price for the service based on a newly designed pricing model. Our proposed pricing model of ApproxDNN guarantees lower or equal cost for service providers compared to the conventional method that solely depends on employment of on-demand instances in case of the reserved instance shortage. We employ real-world traces to conduct an extensive set of experiments and evaluate the performance of our proposed approach. The results show that ApproxDNN reduces the cost of service providers by 18%, while never exceeding the cost of the conventional method and slightly affecting the accuracy by 0.14%.
User Elicited Hand Gestures for VR-based Navigation of Architectural Designs

Virtual Reality (VR) systems have become an affordable mass market technology, along with 3D building design tools such as Building Information Modelling (BIM). This provides architects, designers and their clients with immersive experiences of evolving building designs. VR systems typically require physical controllers to manage 3D navigation and to invoke other functions within the virtual environment. A range of mid-air, contactless gesture detecting technologies exist which offer the potential of more intuitive interactions between a user and the 3D model they are experiencing. This research elicited a mid-air hand gesture set for 3D building navigation from interviews with architects and other design professionals. The gestures proposed by design professionals were evaluated against known usability factors to determine an initial set of gestures which were tested through a VR navigation task. An evaluation study was performed looking at the memorability, intuitiveness and comfort of the gestures. The data from this study suggests that the developed gesture set is effective.
A DLT-based Trust Framework for IoT Ecosystems

An IoT eco-system includes IoT network components, network services and network participants such as organizations, consumers, governments, and businesses. Due to its diversity and scale, trustworthiness is a critical concern to be considered during architectural design and the operational phase of these eco-systems. To do this, security, privacy, reliability, resilience and safety must be assured. However, existing solutions partially address these requirements using centralized approaches that come with challenges such as a single point of failure, scalability, and dependence on a third party. In this context, Distributed Ledger Technology (DLT) and Smart Contracts, due to its intrinsic properties of transparency, immutability, and underlying secure-by-design architecture, allows distributed, decentralized, automated workflows, which can be incorporated to automate the management of the next generation IoT networks. In this paper, we propose a framework for IoT eco-systems providing seamless integration between IoT and DLT to create a decentralized trusted architecture, which ensures trustworthiness of IoT eco-systems at design time and a trust reputation model based on the architecture to protect it during the run-time. Furthermore, we have presented the initial steps towards the implementation of this framework.
Empirical Analysis of Attribute Inference Techniques in Online Social Network

Social network is a popular platform that users share their videos, photos, and other media. To protect the information from being abused for malicious purposes, many social network platforms provide privacy protection mechanisms, such as anonymizing personal identification information, hiding users' profiles that only could be disclosed by social friends, etc. However, recent researches demonstrate that the hidden private attributes still can be inferred by using auxiliary information obtained from social data, e.g., social structures, online behaviors, and correlations among social attributes, etc. Intuitively, most of these methods are sensitive to specific datasets structures and their performance is influenced significantly by the parameter configurations. Thoroughly understanding of the existing inference attacks is very important to develop efficient social data protection solutions. In this paper, we conduct a systematic analysis on typical attribute inference approaches and develop several experiments to evaluate the efficiency of these methods with different social datasets, under different pre-configured environments as well. Our experiment results disclose the impacts on the approach performance caused by different factors, e.g., dataset properties, critical parameters.