Reviewerly/exHarmony huggingface dataset:
Canonical Correlation Analysis

The correlation coefficient measures the linear relation between scalar X and scalar Y. How can the linear relation between vector X and vector Y be measured? Canonical Correlation Analysis (CCA) provides a way. CCA finds a linear combination of X, and a (separate) linear combination of Y, that maximizes the correlation. The resulting maximized correlation is called a canonical correlation. More generally, CCA decomposes two sets of variables into an ordered sequence of component pairs ordered such that the first pair has maximum correlation, the second has maximum correlation subject to being uncorrelated with the first, and so on. The entire decomposition can be derived from a Singular Value Decomposition of a suitable matrix. If the dimension of the X and Y vectors is too large, overfitting becomes a problem. In this case, CCA often is computed using a few principal components of X and Y. The criterion for selecting the number of principal components is not standard. The Mutual Information Criterion (MIC) introduced in Chapter 14 is used in this chapter.
Unsupervised Deep Variational Model for Multivariate Sensor Anomaly Detection

The ever-increasing detector complexity at CERN triggers a call for an increasing level of automation. Since the quality of collected physics data hinges on the quality of the detector components at the time of data-taking, the rapid identification and resolution of detector system anomalies will result in a better amount of high-quality particle data. Therefore, this study proposes CGVAE, a data-driven unsupervised anomaly detection using a deep learning model, for detector system monitoring from multivariate time series sensor data. The CGVAE model is composed of a variational autoencoder with convolutional and gated recurrent unit networks for fast localized feature extraction, long temporal characteristics capturing, and descriptive representation learning. Furthermore, to mitigate signal reconstruction overfitting on anomalous patterns, the CGVAE employs encoded latent feature- and reconstruction-based metrics for anomaly detection. Moreover, the model integrates feature attribution algorithms to explain the contribution of the input sensors to the detected anomalies. The experimental evaluation on large sensor data sets of the Hadron Calorimeter of the CMS experiment demonstrates the efficacy of the proposed model in capturing temporal anomalies.
Research on an Two-Channel ACNN-LSTM Model for Financial Text Sentiment Analysis

This paper proposes a sentiment analysis model based on two-channel attention-driven convolutional neural networks and long short term memory neural networks for financial text. Firstly, this paper uses two different word vector initialization methods to construct classification model by selecting different feature representations and taking full account of the relationship between words. Secondly, this paper adds Attention mechanism based on the context structure to analyze the text to obtain more hidden information. Finally, the experimental results show that our approach is feasible and effective.
Automatic Keyword Extraction in Economic with Co-occurrence Matrix

As the financial market is very sensitive to the sudden news of economic events, it is very important to identify the events in the news items accurately and timely. Keywords can effectively provide information in this aspect. Keywords contain a certain amount of information; people can easily select relative news through keywords. In addition, keyword extraction is an important technology in the field of literature retrieval and Web search. This paper proposes a keyword extraction method based on a co-occurrence matrix. First of all, based on the dictionary method, we collect more comprehensive information of Chinese characters and automatically segment Chinese words according to the bidirectional maximum matching method. At the same time, we can extract the words not included in the dictionary as a separate unit. Then, the co-occurrence matrix is constructed to obtain the co-occurrence frequency of different phrases. Finally, we get keywords according to the co-occurrence frequency of phrases. Compared with the traditional TD-IDF method, this method has better accuracy and extracts more effective keywords in the face of a single paragraph. The experimental results show that this method can extract more effective keywords in some single paragraphs, and the experimental results are satisfactory.
Single Image Defogging Algorithm-Based on Modified Haze Line Prior

In this paper, an effective single image defogging algorithm is proposed for the natural and synthetic datasets. The proposed algorithm is based on modified haze line prior technique for calculation of transmission map and atmospheric light parameter. Transmission map calculated using haze line prior is not refined because of some halos and artifacts. Due to this reason, the output obtained using haze line prior is not sharp and visually not appealing. In the proposed algorithm, guided filter has been introduced to overcome this problem. Both qualitative and quantitative comparisons have been done with some of the existing defogging methods to establish effectiveness of proposed method. The key benefit of the proposed algorithm is its capability to restore the sharpness of a foggy image without any post-processing and pre-processing of the image.
Conception of Applying Sovereign Blockchain Technology to Improve Tax Credit Management in Guangdong Province, China

Presently, several problems still exist in tax credit management including inadequate collection of credit information, opaque process of credit rating evaluation and adjustment, insufficient pertinence of credit publicity results, and low social application degree of credit evaluation results. The application of blockchain technology can perfectly match the needs of information management because it has several advantages such as decentralization, openness and transparency, non-tampering and traceability, and intelligent contract mechanism. Therefore, the technology can be used to achieve the flow of information data assets and privacy protection, and has good applicability to solve the problems faced in the current tax credit management process. This paper aims at solving the existing problems in tax credit management based on the analysis of the characteristics of the blockchain technology and the compliance of tax credit management using Guangdong Province as an example. The paper suggests the specific idea of applying the sovereign blockchain technology to improve the level of tax credit management.
Mixing Consistent Deep Clustering

Finding well-defined clusters in data represents a fundamental challenge for many data-driven applications, and largely depends on good data representation. Drawing on literature regarding representation learning, studies suggest that one key characteristic of good latent representations is the ability to produce semantically mixed outputs when decoding linear interpolations of two latent representations. We propose the Mixing Consistent Deep Clustering (MCDC) method which encourages interpolations to appear realistic while adding the constraint that interpolations of two data points must look like one of the two inputs. By applying this training method to various clustering (non-)specific autoencoder models we found that using the proposed training method systematically changed the structure of learned representations of a model and it improved clustering performance for the tested ACAI, IDEC, and VAE models on the MNIST, SVHN, and CIFAR-10 datasets. These outcomes have practical implications for numerous real-world clustering tasks, as it shows that the proposed method can be added to existing autoencoders to further improve clustering performance.
CA-NCF: A Category Assisted Neural Collaborative Filtering Approach for Personalized Recommendation

In the big data environment, the sparsity problem of collaborative filtering recommendation algorithm becomes increasingly serious, which has a great impact on the accuracy of recommendation. In some recent researches, item categories were input into neural networks to enrich the embedded information in the process of training. However, these methods generally simultaneously use item categories and items as embedded information, which may weaken the importance of item categories. Therefore, this paper proposes a neural collaborative filtering method based on category assistance. In this method, the interaction between item category and user is first modeled by Neural Matrix Factorization ((Neu-MF)), which raises the impact of item category in the relationship extraction between items and users. Then, only the items in the trained results of categories are used in an optimized Neural Collaborative Filtering (NCF) framework for item recommendation. Based on the real ecommerce data set from Alibaba, experimental results show that this method obtains better result in the Hit Rate (HR) and the Normalized Discounted Cumulative Gain (NDCG) compared with other baseline methods.
Handwritten New Tai Lue Character Recognition Using Convolutional Prior Features and Deep Variationally Sparse Gaussian Process Modeling

New Tai Lue is widely used in Southwest China and Southeast Asia. Hence, it is important to study related handwritten character recognition. Considering the many similar characters in handwritten New Tai Lue, this paper proposes an offline handwritten New Tai Lue character recognition method based on convolutional prior features and deep variationally sparse Gaussian process (DVSGP) modeling. An offline handwritten database is constructed, a convolutional neural network is trained to extract the convolutional features of New Tai Lue character images as prior features, and a DVSGP model is built. The extracted features are input into the DVSGP model to construct a recognition model. The experimental results show that the accuracy of the model is 97.67% and that the precision, recall, and F1-score are 0.9769, 0.9767, and 0.9767, respectively, which are better than those of other methods. The proposed method also achieves high accuracy on the MNIST recognition task, verifying its universal applicability.
Paradigmatic development of science

Paradigmatic development of science This paper examines the paradigmatic evolutionary development of science. The authors rely on the works of T. Kuhn, then, following V. S. Stepin, there are four shifts of research paradigms. The dependence of the research results on the scientist’s orientation towards one or another paradigm is emphasized. The article suggests that the subsequent scientific paradigm will focus on what is called “Nothing” in philosophical terminology, i.e. on the dark energy of the cosmic vacuum, it is also called the darkness of the creation of the world. It is noted that before that, science focused mainly on real-life matter. The movement of thought in this direction will shed light on the riddle of the emergence of the material from the formlessness of the nonexistent.
SMIM Framework to Generalize High-Utility Itemset Mining

In high-utility itemset mining (HUIM), the utility of a set of items is calculated as the sum of the utilities of the individual items. In this paper, we describe scenarios where utility may be less than this sum for multi-item itemsets. To overcome the limitation of the current itemset mining algorithms for such scenarios, we introduce the SMIM framework for itemset mining in which utilities are constrained to be non-negative subadditive and monotone functions over itemsets. SMIM generalizes HUIM, can be used to analyse transaction databases with multi-item discount schemes, and can further be used to mine interesting patterns in a social network dataset. Finally, we explain how to design algorithms for SMIM with any general subadditive monotone utility function.
Building structured Knowledge Base for trustable NLP application systems by Resource by Collaborative Construction scheme

Wikipedia is an exhaustive resource that contains too much information for any one human to completely absorb. Computers, on the other hand, are able to trawl through information at a rapid pace. However, as Wikipedia is written in such a way that it is clear for people to read, it is not possible for a machine to easily utilise and manipulate this information. Dr Satoshi Sekine is based within the Language Information Access Technology Team at the RIKEN Center for Advanced Intelligent Project, Japan. He is the team leader of a project called SHINRA that is centred around a Resource by Collaborative Contribution (RbCC) scheme and seeks to build a structured knowledge base that combines Wikipedia and an Extended Named Entity (ENE). The RbCC scheme enables the team to work together to construct a resource to advance the field of deep learning. ENE is a hierarchy that is divided into name, time and numerical expressions and the team found that, generally speaking, any question on any specific matter fits within one of these three categories and means that information can potentially be understood by machines engaged in deep learning. The researchers worked to assign appropriate concept class labels and then used references to existing online thesaurus entries and ontology sites to find information that matched the hierarchy. Ultimately, Sekine and the team want to build a structured Wikipedia and to do this categorisation, attribute extraction and attribute value linking will need to be performed.
Activating the Active Learning Classroom

This chapter is focused on attracting students’ interest and engagement in global environmental issues. Jhenyi Wu discusses methods to better engage undergraduate students so that they transfer their motivation from mobile games on smartphones to fully engaged classroom discussion. Can such students be further motivated to take real actions? This chapter addresses such questions in an active learning classroom, which attempts to step out from a local city environment in Taiwan to important situations around the world.
Image Encryption Based on Random Fractional Discrete Cosine Transform and Coupling Map Lattices with Hybrid Chaotic Mapping

To improve the security of image encryption based on reality-preserving fractional discrete cosine transform (RPFrDCT), an image encryption scheme based on random fractional discrete cosine transform (RFrDCT) is proposed. Random orders and generating sequences (GS) in RFrDCT not only overcome the weakness of low sensitivity of the fractional orders in RPFrDCT based image encryption, but also enlarge the key space of the encryption scheme. Hybrid chaotic mapping based on Tent and Sine maps is applied to generate the random fractional orders, the random GS and the diffusion matrix. Experimental results show that the encrypted image has better security performance.
The Optimization of Multi-classifier Ensemble Method Based on Dynamic Weighted Voting

Abstract Generally, on the same data set, different deep learning classification models will achieve different performances. The existing weighted voting method can combine the results of models, which can improve the performance of classification. On the other side, its classification accuracy is affected by the accuracy of all models. In this paper, we proposed a dynamic weighted voting method. Our method dynamically selects models on different data sets, and integrates them according to their weights, thereby improving the classification accuracy. We evaluated the methods on three data sets of CIFAR10, CIFAR100 and Existing, which increased the accuracy about 0.65%, 0.91%, and 0.78% respectively compared with the existing weighted voting method.
Big Book: An Attractive Way to Comprehend Reading Text for Young Learners

Introducing reading text from young learners should be supported by appropriate facilities and resources adequate infrastructure. Learning Reading should be managed in such a way as to create meaningful and enjoyable learning activities. The use of big book as a medium of learning is one of the efforts to understand the reading text for young learners. This research aims at knowing the application of Big Book in teaching student’s reading comprehension of the second grade students of elementary school. The research design in this study is qualitative approach. The data are collected by using observation, interview, and documentation. Meanwhile, techniques of analzying data are data reduction, data display, and drawing conclusion. The subject of the research is the second grade students, which consists of 13 female and 7 male. The researcher is successful to use big book to teach reading comprehension. The students feel happy and interested when using big book. The researcher asks the students to read aloud the reading text individually and in a group, and then gives questions about the content of the text. Students are able to understand the reading text using big book. Based on the findings of research, the researcher also recommends conducting further research on using of Big Book.
Investigating the leadership style of managers in the physical education unit of the Education Department of Babol city and its relationship with organizational effectiveness

The aim of study is Investigating the leadership style of managers in the physical education unit of the Education Department of Babol city and its relationship with organizational effectiveness. Statistical society includes all PE teachers in Physical Education Department of Education office about 195 persons. Sample volume has been determined by Cochran's formula about 131 persons. Study method is of descriptive- correlation one. Research type is by applicable and method of gathering data is field one. We have used of questionnaire instrument to gather information. Above questionnaire narration has been approved by some professors in management and sport management field. Questionnaire stability after gathering 20 questionnaires of above society for leading style is (α=0.82) and organizational impression (α=0.85). Data analysis has been done in the two levels of descriptive and inferential and by the aid of SPSS22 software. Also, we have used of inferential test Pearson test, regression. The result of study shows, there is positive and meaningful relation among leading style and organizational impression (p
Causal Inference

Statistical models—including all statistical network models discussed in this book—describe probabilistic relationships between variables. They can tell us that if X is high, Y will likely also be high, but they cannot tell us what would happen to Y if we were to increase the value of X. In order to model the outcome of interventions, we need to go beyond statistical inference towards causal inference. This chapter introduces the reader to the core tenets and assumptions of causal inference from observational data. The reader will learn how to use directed acyclic graphs (DAGs) to express causal relations; what type of causal relations between variables imply which type of probabilistic relations and how to assess this using d-separation; how to assess, in a principled manner, whether a causal effect is confounded; how to compute causal effects using structural causal models (SCMs); and how to (possibly) discover causal relations from data.
Acknowledgment of APP Reviewers for 2021

Asian Politics & PolicyVolume 14, Issue 1 p. 147-149 MISCELLANEOUS Acknowledgment of APP Reviewers for 2021 First published: 07 February 2022 https://doi.org/10.1111/aspp.12627Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume14, Issue1January 2022Pages 147-149 RelatedInformation
Importance Performance Analysis (IPA) on Website Quality (Webqual): An Evidence from A Higher Education Institution in Indonesia

This paper attempts to evaluate the quality of website of the Faculty of Economics and Business of University X in Indonesia, using the WebQual model and map it using Importance Performance Analysis. The study uses a quantitative method of Importance Performance Analysis (IPA), by using an online survey with a WebQual questionnaire. Samples were collected through an accidental sampling, involved 70 samples, collected from the stakeholder of the faculty, representing students, lecturers, partners as well as general public.&#x0D; The result of the study reveals that Information Quality poses the most performed of the Website Quality while Usability was the least ones. The result recommends any higher education institution to evaluate information provided in their website to its stakeholder, should consider its usability as it is the most important factor which is needed by website visitors.
Index/Impressum: Vakuum in Forschung und Praxis 1/2022

Vakuum in Forschung und PraxisVolume 34, Issue 1 p. 51-51 MagazinFree to Read Index/Impressum: Vakuum in Forschung und Praxis 1/2022 First published: 08 February 2022 https://doi.org/10.1002/vipr.202270109AboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat No abstract is available for this article. Volume34, Issue1February/March 2022Pages 51-51 RelatedInformation
Short Introduction to R

This chapter discusses the basics behind working with R and RStudio, explains how to install and load packages in R, as well as how to find help for different R functions. Furthermore, readers will learn how to construct matrices in R, load and inspect data, select rows and columns of a data set, and compute variance-covariance matrices in R.
Learning Weighted Automata over Principal Ideal Domains

In this talk we discuss L*-based automata learning algorithms for weighted automata over a semiring. We provide a general adaptation of the algorithm and show that it works correctly when the semiring is a principal ideal domain. We also show that termination fails in general for an arbitrary semiring, in particular the natural numbers.
Refinement Image Regeneration

With the recent spurt in technological advancements, especially the advent of autonomous navigation, image processing has become ubiquitous in everyday life. Image reconstruction is an imaging application aimed at rebuilding images from a variety of features obtained from sparse input ground truths or input sources. In this work, we present an image reconstruction method to recreate and represent detailed images solely from binary edges and flat color using Generative Adversarial Networks (GANs) for a universal application, whereas all major recent breakthroughs are related to either medical imaging or noise removal. Our work has yielded promising results and we were able to obtain realistic images of objects and human faces with original as well as altered feature sets. We present here the results with both qualitative and quantitative comparisons with some benchmark frameworks.