Reviewerly/exHarmony huggingface dataset:
The Hydrophobicity Class of Porcelain Insulator Detection Based on Digital Image Processing : A Paper Review

The experiment of quality assurance of polymeric outdoor insulators service life is necessary to be checked the class of wettability by using the digital image processing tools. The image processing can improve its pictorial information for human interpretation, render it more suitable for autonomous machine perception. This paper provides a survey of the recent technology and theoretical concept to explain the development of the Hydrophobicity Class (HC) of porcelain insulator. The hydrophobic properties of the housing material were found degraded to a different extent between field-aged insulators due to differences in material structure and pollution conditions. This paper contributes to the latest development of reviews relating to image processing for the implementation of porcelain insulator hydrophobic systems testing., and their related studies. We categorized the computer vision mainstream into 2 group such as Machine Learning Methods Applied to Digital Image Processing and Deep Learning Methods Applied to Digital Image Processing. Then compare pros/cons in each type of technique. We also provide brief explanation on the up-to-date information about the techniques and their performance.
Planning and Practice of Smart Campus in Higher Vocational Colleges from the Perspective of Education Informatization 2.0

With the progress of information technology and the development of higher vocational education informatization 2.0, the digital campus construction of higher vocational colleges has entered the stage of smart campus construction. Smart campus is mainly characterized by integrating cloud computing, Internet of Things, big data, artificial intelligence and other intelligent elements, realizing the intelligent upgrading of information from environment, resources to activities, and providing a comprehensive perception and intelligent campus information ecological environment. Based on an example of the construction of smart campus in Urban Vocational College of Sichuan, the author discusses the construction objectives, ideas, contents and expected results of smart campus, hoping to provide some references for the construction of smart campus in other higher vocational colleges.
Quantum Algorithm for Solving the Continuous Hidden Symmetry Subgroup Problem

Quantum computers are expected to be able to outperform classical computers. In fact, some computational problems such as integer factorization can be solved on quantum computers substantially faster than classical computers. Interestingly, these problems can be cast in a framework of the hidden symmetry subgroup problem. However, only a few of quantum algorithms for efficiently solving this problem have been known, and the approaches used in all previous results can be applied to particular groups with specific group actions. In this paper, we introduce new technique for solving the hidden symmetry subgroup problem which can be applicable for any groups and any group actions with a certain condition. In addition, we define the continuous hidden symmetry subgroup problem on a group by employing a continuous oracle function, and prove that if the group is a metric space and the group action satisfies some condition, then the continuous hidden symmetry subgroup problem can be efficiently reduced to the continuous hidden subgroup problem. In particular, we show that there exists an efficient quantum algorithm to solve the continuous hidden symmetry subgroup problem on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> , while it has not yet been shown that the original hidden symmetry subgroup problem on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mathbb {R}^{n}$ </tex-math></inline-formula> can be efficiently solved by a quantum computer.
Representational-Interactive Feature Fusion Method for Text Intent Matching

This paper introduces a new architecture for text intent matching method, which fuses deep representational and deep interactive features, then the combined features are trained finally by a single reasoning network model. The architecture mainly includes two fusion typical sub models based on the fusion of separated features. Then, we defined two novel loss functions for the models, and they have very excellent performance in the experiments. More importantly, the F1-Score of our model improves by 2.6% than the art-of-the-methods on the well-known datasets.
An Analysis of Interpersonal Function in TED Educational Speeches

TED (Technology, Entertainment, Design), an online speech platform, has become popular around the world in recent years. With the mission of “ideas worth spreading”, TED speakers are committed to using languages to convey ideas and to influence the attitudes and the behaviors of their audiences. This mission actually reflects the interpersonal function of TED speeches. In order to explore how interpersonal function is realized in TED educational speeches, the paper selects the British educator Ken Robinson's three TED speeches as research materials. The study will elaborate on how interpersonal function is realized in TED educational speeches through mood, modality and personal pronoun systems. The study reveals that in mood system, declarative mood is always used in TED educational speeches to express statement or suggestions so as to enhance the credibility of the speeches. In modality system, median and low value modal operators is always used by the speakers in order to convey opinions in a polite and gentle way. In personal pronoun system, the speakers always use first personal pronouns in their deliveries, which is conducive to present information and bridge the gap between the speaker and the audiences.
Appraise of Deep Learning and Image Processing based Single Image Dehazing Algorithms

The atmospheric particles tend to either absorb or scatter the light rays. This phenomenon is known as the haze by which light and other natural characteristics are obstructed. Image dehazing is an image enhancement approach that refers to procedures that attempt to eliminate the haze amount from a hazy image. The single image dehazing is a dehazing approach that uses a single reference image for dehazing. Dehazing methods are majorly classified into two main categories, one of them being prior based where most of the methods are linear and calculation heavy which removes haze on the most prominent features. While the other one is data-oriented which understands the image features and extracts them and converts them either in some useful maps or in some feature maps. Both methods are efficient in their view and both have some limitations in them. This paper presents a review of several single image haze removal methods. The review covers different prior-based and data-oriented approaches for image haze removal.
Designing for Aesthetic Experience

Digital ways of representing fine art are significantly changing the way art is put on display. New exhibition formats including digital projections of fine art allow us to rethink how we can use technology to create art experiences. However, replicating the existing relationships between art, meta-text and visitor might not be the right way. In this project, I explore new ways of creating technologically enabled experiences with fine art paintings based on John Dewey’s concepts of aesthetic experience and expression. Utilizing a constructive design research approach, I create concrete prototypes and designs that expand research on aesthetics of interaction to propose new ways for museums to create art experiences.
Intelligent Software-Aided Contact Tracing Framework: Towards Real-Time Model-Driven Prediction of Covid-19 Cases in Nigeria

As many countries around the world are trying to live with the deadly coronavirus by adhering to the safety measures put in place by their government as regulated by World Health Organization (WHO), it becomes very vital to continuously trace patients with COVID-19 symptoms for isolation, quarantine and treatment. In this work, an intelligent software-aided contact tracing for real-time model-driven prediction of COVID-19 cases is proposed utilizing COVID-19 dataset from kaggle.com. The dataset is preprocessed using One-Hot encoding and Principal Component Analysis. Isolation Forest algorithm is used to train and predict COVID-19 cases. The performance of the model is evaluated using Accuracy, Precision, Recall and F1-Score. The intelligent software-aided contact tracing framework has four layers: symptoms, modeling/prediction, cloud storage/contact routing and contact tracers. The contact tracing system is an android application that receives symptom values, predict it and automatically send the prediction result together with user’s contact and location details to the closest contact tracer via the Firebase real-time database. The closest contact tracer is determined by employing a dynamic routing algorithm (contact routing algorithm) that uses Open Shortest Path First (OSPF) protocol to compute the distance between two geographic locations (user and contact tracer) and chooses a contact tracer with shortest distance to the patient utilizing a unicast routing technique (routing a patient to a contact tracer in a one-to-one relationship). The predictive model along with the android application for software-aided contact tracing is implemented using the python, and Java programming language on Pycharm and Android Studio IDE respectively. This Framework is capable of predicting COVID-19 patients, notifying contact tracers of positive cases for proper follow-up which can subsequently curtail the spread of the virus.
Automatic Image Colorization via Weighted Sparse Representation Learning

Automatic image colorization is to generate a colorful image from a given gray image automatically. It is an ill-conditioned task and remains a challenging problem in computer vision. One main challenge in example-based image colorization is how to find the correct correspondence between the grayscale image and the reference color image. In this paper, we propose a novel automatic example-based image colorization method via weighted sparse matching. First, we segment the images into superpixels, and operates at the superpixel level rather than pixels. Then we extract intensity features and texture features for each superpixel, which are then concatenated to form its descriptor. The feature descriptors collected from the reference image composes the representation dictionary. Finally, the correspondence between target grayscale image and the colorful reference image is built by solving a weighted sparse representation learning problem, and the target superpixels are colorized based on the chrominance information from the corresponding reference superpixels. Experimental results demonstrate that our colorization method outperforms several state-of-the-art methods.
Auto-analysis of writing database based on corpus model

Abstract The paper presents corpus-based writing database analysis and its application to college English writing classes. A small writing database was built by collecting 88 students’ 264 writing samples in one semester. Corpus model was used in the research for analysing connector using, cluster score and word frequency. The contrastive connector analysis reveals the difference between the collected samples and the existing Corpus SWECCL. The cluster and word frequency analysis introduces a new approach to analyse writing samples. The paper also details some classroom activities designed from research findings. Students’ in-class performance and after-class feedback both indicate that they are interested in the analysis-based classroom activities. At the beginning and the end of the semester, the study collected students’ English writing scores in the tests and adopted SPSS to assess their achievements. The paper ends with a short reflection on the enlightenments and limitations of the research.
New cryptographic hardness for learning intersections of halfspaces over boolean cubes with membership queries

We revisit the PAC learnability of the class of intersections of polynomially many halfspaces over boolean cubes in the membership query model. The previous works (Klivans-Sherstov 2009, Angluin-Kharitonov 1995) imply the unlearnability of this class based on cryptographic assumptions, which is established in the case that the learner cannot generate queries of positive labels. We investigate this issue, focusing on the case that the learner is given representations of input distributions (so it may generate queries of positive and negative labels). Our result is that assuming some new cryptographic primitives, the class is still unlearnable in the query model even if the learner has representations of the distributions. The result is established via a new argument. We show that if the class is learnable, we can come up with a differing-inputs obfuscator, which, however, does not exist given the cryptographic primitives. Thus the hardness result follows from the contradiction.
Radar Tasks Scheduling Based on Adaptive Neuro Fuzzy Inference and Time-Interval Planning

The paper presents a new task scheduling model for phased array radar based on priority assessment of task request. The method is used to analyses the radar tasks choosing process for different kinds of targets. Considered with the work mode and task of the multi-function radar, a task scheduling model using Adaptive Neuro Fuzzy Inference System (ANFIS) was adopted which preprocess the interfering factors of task priority such as target working mode, parameter and amount et al. which help the scheduler finds the task to be executed. The scheduling policies that give better priority to the most important and critical targets. An adaptive time length of scheduling model is used to adjust dynamically time and energy resources of multi-function radar. The proposed method can effectively improve the performance of the radar scheduler and make the radar tasks scheduling more feasibly.
Quantum Codes Constructed by Quantum Caps in PG (4, 5)

Abstract In this paper, 57 quantum caps in PG (4,5) are found using a computer-supported search method, and related quantum error-correcting codes are constructed via CSS construction. By comparison, most of these codes have good parameters, and some of them enhance the existing results.
Are User Manuals Obsolete with Persuasive Systems?

Ensuring that users can successfully perform their primary tasks and achieve their behavioral goals is critical for the success of persuasive system. Are user manuals needed to achieve this goal? In this paper, we sought to understand the role of end-user documentation for persuasive systems. 50 persuasive apps from the Google Play Store were analysed to determine the importance of user manuals or guides for persuasive health apps. Although based on a small data set, our results show that in most cases persuasive systems need user documentation and hence we encourage developers of such systems to allocate resources for creating quality documentation for their users.
Word Research on Sentiment Analysis of Foreign News Reports Concerning China Based on the Hybrid Model of Opinion Sentence and BLSTM-Attention

This paper proposes a sentiment analysis model based on a mixture of opinion sentences and BiLSTM-Attention mechanism, which is used to solve the problem of increasing foreign news reports concerning China in recent years that urgently need computers to assist in judging the sentiment polarity of China in reports. Firstly, text analysis is performed on the article to extract emotional sentences with emotional expression. Combined with the deep learning method for identification and judgment. Finally, use the foreign media reports on China in the actual project database as experimental data, use 70% of manual labeling and 30% for experimental verification. The experimental results show that compared with some existing methods, this method has a higher accuracy in the sentiment judgment of Chinese-related articles.
Simultaneous Gradient Descent-Ascent for GANs Minimax Optimization using Sinkhorn Divergence

The Sinkhorn divergence, a smooth and symmetric normalization version of entropy-regularized optimal transport (EOT) is a promising tool for Generative Adversarial Networks (GANs). However, understanding the dynamic of gradient algorithms for Sinkhorn-based GANs remains a big challenge. In this work, we consider the GANs minimax optimization problem using Sinkhorn divergence, in which smoothness and convexity properties of the objective function are critical factors for convergence and stability. We prove that GANs with convex-concave Sinkhorn divergence can converge to local Nash equilibrium using first-order simultaneous stochastic gradient descent-ascent (SimSGDA) algorithm under certain approximations. We further present a nonasymptotic analysis for the convergence rate of the SimSGDA using structural similarity index measure (SSIM). Our experiments suggest a convergence rate proportional to the inverse number of SGDA iterations tested on tiny-colored datasets (Cats and CelebA) and advanced neural architectures (DCGAN and ResNet). We demonstrate that SSIM is potential tool to measure convergence rate of the SimSGDA algorithm empirically.
An Extensible Network Traffic Classifier Based on Machine Learning Methods

The rapid development of telecommunications creates a lot of new types of the traffic. This cause a great problem for real time traffic classification using Machine Learning Methods due to their inability to add new classes. Supervised Learning methods are unable to classify samples that are not represented in train sequences. Unsupervised Learning methods require a significant amount of time to build a model, give relatively low results and the resulting clusters are difficult to interpret. The paper proposes a new approach to real-time traffic classification. The uniqueness and value of our model lies in its ability to expand, i.e. add new classes in real time. For implementation of proposed algorithm a set of techniques was used: a unique feature matrix for classification by the first 15 packets; fast high-precision classification by Random Forest and XGBoost methods; a matrix of distances for clustering obtained using Extremely Randomized Trees based on known flows; detection of new classes with Agglomerative clustering through the minimum distance between clusters; determination of the nearest neighbors of the cluster for traffic engineering; automatic updating of the classifier based on clustering results. The experimental part of the study confirms the success of the proposed methodology.
Designing Blended Experiences

The increasing ubiquity of interactions as a mix between digital content and physical objects and spaces, brings about new challenges for designers. There is a need to embed digital systems in physical places, whether those are existing physical structures or existing digital platforms. Traditional approaches to product design, interaction design and user experience design do not often take this new context into account. They do not consider how designers produce new digital and physical experiences that work harmoniously to provide new forms of engagement. To address this, we illustrate the constructs of blended experiences and how they can be used in the context of bridging green spaces between different countries. We propose the idea of blended experiences and offer a framework of constructs and techniques that can help designers work in this emerging area of design.
Actualization of reflexive and didactic potential of students’ independent learning activities in the learning process

The change in educational paradigms has led to the need to define new methodological regulations that allow to consider the objects of pedagogical reality from a different angle. This led to the need to study traditional issues of pedagogy in an innovative context. The issue of forming students’ independent learning activities is not new for pedagogy. However, we present it from the perspective of an integrative-reflexive approach, which allowed us to identify its internal potential for personal development. The theoretical methods of pedagogical research used in the study: analysis, synthesis, comparison, generalization, method of causal relationships research, etc., which allowed to mentally penetrate into the essence of the studied pedagogical phenomenon and rethink it in a new educational reality. It is established that the educational independent activity accumulates the reflexive and didactic potential for professional and personal formation and development in the process of studying at the university. The possibilities of reflexive discourse as a way of realizing the reflexive-didactic potential of educational independent activity in the learning process are determined. According to the structure of the process of reflection in educational independent activity we distinguish the stages of reflexive discourse: reflexive-indicative, reflexive-presentative and reflexive-realizational. We consider the relationship of these stages of the discourse with various types of reflection and features of self-assessment, self-analysis, self-design and self-realization as structural components of educational independent activity.
Investigation on Industry Applications of Blockchain Technology

Blockchain (BC) is a collective, indisputable ledger that makes easy the course of action of recording dealings and footpath possessions in an industry system. On the other hand, BC is significant in business that sprints on information. A BC network can follow instructions, expenditure, financial records, manufacture, and many more. Yet, the difficulties of BC in industry like mindfulness and getting, association, culture, cost and proficiency, regulation and administration, and security and protection must be properly dealt with to gain the advantages fully. Hence, the objectives of this chapter are to investigate various applications of industries that involve BC technology, the challenges, evolution of BC in industry applications, and its need, types of BC, and networks for industries and comparison. Further, the chapter discusses how to manage the hyperconnected supply chain in terms of industrial applications in terms of scalability, integrity, and legacy.
$$l _{2,p}$$-norm Based Efficient Linear Discriminant Analysis for Feature Extraction

Traditional linear discriminant analysis (LDA) is one well-known statistical method for feature extraction and dimensionality reduction, and is sensitive to the outliers. The newly proposed robust LDA (RLDA) presents an effect method to overcome this problem by using the $$l _{2,1}$$ -norm to replace the $$l _{2}$$ -norm used in loss function. However, the rigid $$l _{2,1}$$ -norm based RLDA do not have enough flexibility to handle the complex structure of data in practical applications, and it has very low efficiency for high-dimensional data due to its eigen-decomposition based solution method. In this paper, RLDA is extended in the sense of $$l _{2,p}$$ -norm ( $$0<p\le 2$$ ) to improve its performance, and an effect strategy is proposed to reduce the running time of the algorithm by introducing a dimension-reduced preprocessing. Experiments results on benchmark data sets show that our method is very competitive in comparison with the RLDA and other state-of-art feature extraction algorithms in terms of classification accuracy and efficiency.