Reviewerly/exHarmony huggingface dataset:
Design and Validation of an Explainable Fuzzy Beer Style Classifier

We describe step by step how to design, implement and validate an interpretable fuzzy rule-based beer style classifier endowed with explanation capability. First, we revise some preliminary work regarding both interpretable fuzzy modeling methodologies and related software. Second, we introduce the use case on beer style classification. Third, we build and validate a fuzzy rule-based classifier with a good interpretability-accuracy trade-off for this use case. Fourth, we endow this classifier with explanation capability through a general linguistic interface that is tuned ad-hoc for the use case under consideration. Fifth, we show how the designed explainable classifier can be refined and exploited with several interoperable software tools. Finally, we compare two kinds of multi-modal (i.e., textual and graphical) explanations: (1) explanations which are inherently natural and fully-meaningful to users, because they are supported by an interpretable fuzzy rule-based classifier which is carefully designed (i.e., it is grounded in common-sense expert knowledge and global semantics); and (2) explanations which are supported by the linguistic approximation of a fuzzy rule-based classifier extracted from data with the focus only on accuracy (thus lacking of linguistic interpretability). The use case is implemented with open source software and all related datasets, tools and scripts are available online for the sake of reproducibility.
A Paragraph-level Multi-task Learning Model for Scientific Fact-Verification.

Even for domain experts, it is a non-trivial task to verify a scientific claim by providing supporting or refuting evidence rationales. The situation worsens as misinformation is proliferated on social media or news websites, manually or programmatically, at every moment. As a result, an automatic fact-verification tool becomes crucial for combating the spread of misinformation. In this work, we propose a novel, paragraph-level, multi-task learning model for the SciFact task by directly computing a sequence of contextualized sentence embeddings from a BERT model and jointly training the model on rationale selection and stance prediction.
Meta Learning to Classify Intent and Slot Labels with Noisy Few Shot Examples

Recently deep learning has dominated many machine learning areas, including spoken language understanding (SLU). However, deep learning models are notorious for being data-hungry, and the heavily optimized models are usually sensitive to the quality of the training examples provided and the consistency between training and inference conditions. To improve the performance of SLU models on tasks with noisy and low training resources, we propose a new SLU benchmarking task: few-shot robust SLU, where SLU comprises two core problems, intent classification (IC) and slot labeling (SL). We establish the task by defining few-shot splits on three public IC/SL datasets, ATIS, SNIPS, and TOP, and adding two types of natural noises (adaptation example missing/replacing and modality mismatch) to the splits. We further propose a novel noise-robust few-shot SLU model based on prototypical networks. We show the model consistently outperforms the conventional fine-tuning baseline and another popular meta-learning method, Model-Agnostic Meta-Learning (MAML), in terms of achieving better IC accuracy and SL F1, and yielding smaller performance variation when noises are present.
OTOSO: Online Trace Ordering for Structural Overviews

Identifying structures in data is an essential step to enhance insights and understand applications. Clusters and anomalies are the basic building blocks for those structures and occur in various types. Clusters vary in shape and density, while anomalies occur as single-point outliers, contextual or collective anomalies. In online applications, clusters even have a higher complexity. Besides static clusters, which represent a persistent structure throughout the whole data stream, many clusters are dynamic, tend to drift and are only observable in certain time frames. Here, we propose OTOSO, a monitoring tool based on OPTICS. OTOSO is an anytime structure visualizer, that plots representations for density-based trace clusters in process event streams. It identifies temporal deviation clusters and visualizes them as a time-dependent graph. Each node represents a cluster of traces by size and density. Edges yield information about merging and splitting trace clusters. The aim is to provide an on-demand overview over the temporal deviation structure during the process execution. Not only for online applications, but also for static datasets, our approach yields insights about temporally limited occurrences of trace clusters, which are difficult to detect using a global clustering approach.
A Framework for Long-Term Tracking Based on a Global Proposal Network

Deep learning technology has greatly improved the performance of target tracking, but most recently developed tracking algorithms are short-term tracking algorithms, which cannot meet the actual engineering needs. Based on the Siamese network structure, this paper proposes a long-term tracking framework with a persistent tracking capability. The global proposal module extends the search area globally through the construction of a feature pyramid. The local regression module is mainly responsible for the confidence evaluation of the candidate regions and for performing more accurate bounding box regression. To improve the discriminative ability of the regression network, the error samples are eliminated by synthesizing the temporal information and are then classified through a verification module in advance. Experiments on the VOT long-term tracking dataset and the UAV20L aerial dataset show that the proposed algorithm achieves state-of-the-art performance.
Hybrid Approach for the Semantic Analysis of Texts in the Kazakh Language

In this paper authors propose a hybrid approach for semantic analysis of text resources and documents in the Kazakh language. An overview and difficulties of analysis for the Kazakh language are presented. The developed approach consists of two main parts. The first definition of keywords (phrases) from the text, and the second, based on the data obtained, will build an annotated summarization of the text. To implement the first part of the approach, the TF-IDF algorithm was applied to extract keywords and phrases from texts. The cosine similarity of the sentence data in the Kazakh language was calculated to determine the similarity. With the help of certain similarities semantic links in the text are determined. On the basis of the data obtained, the second part is performed - the abstraction of texts. The number of annotations directly depends on the size of the document. The linguistic corpus of the Kazakh language was collected for carrying out experiments and calculations. A study of various approaches and a hybrid approach for the semantic analysis of the Kazakh language was carried out. The practical part was implemented in Python. The article presents the results of experimental calculations.
An Enhanced Convolutional Inference Model with Distillation for Retrieval-Based QA

A common solution of automatic question-answering (QA) systems is retrieving the most similar question for a given user query from a QA knowledge base. Even though some models have got promising performance on this task, it may be hard for them to achieve a balance between accuracy and efficiency. In this paper, we propose an enhanced convolutional inference model with StructBert distillation, called StructBert-ECIM, to achieve such balance.
Application of GeoGebra media in teaching the concept of particle kinematics in 1D and 2D

This article is a result of reference analysis about how to use GeoGebra software in learning physics. Specifically in this paper explained how to use GeoGebra to teach the concept of particle kinematics in 1D and 2D. The discussion is focused on straight motion (1D) and parabolic motion (2D) concepts. Each discussion is accompanied by equations of straight motion or parabolic motion and also displays the results of making graphs with GeoGebra to make it easier for readers to understand. The results of the analysis can be concluded that the use of GeoGebra software can be easily used by students if they have been equipped with mathematical concepts and knowledge about graphics. The inference from the results of this analysis is that it is time for teachers to learn and be able to use computer-based and applicative software.
RETRACTED: Evaluation of college students’ English performance considering Roche multiway tree clustering

In this study, the questionnaire survey, diversified classroom teaching, and multivariate evaluation are carried out on college students of non-English majors based on the Roche multiway tree clustering method. The SPSS statistical software is used for statistical analysis. The results show that Roche multiway tree cluster distribution of non-English major college students is at a medium level. There is no significant correlation between the comprehensive English performance and the overall Roche multiway tree clustering, and there is a low positive correlation between Roche multiway tree clustering and English listening. Studies have suggested that in comprehensive English classroom teaching, teachers should pay attention to the weak intelligence of students, teach students according to their intelligence difference, build a diversified classroom teaching evaluation model, and promote the all-round development of student intelligence.
Dynamic Euclidean Geometry: pseudo-Toulmin modeling transformations and instrumental learning trajectories

The present paper attempts to bridge the world of DGS technology with the world Euclid bequeathed to us in his "Elements".Competence in the DGS environment depends on the competence of the cognitive analysis as students seek to decode their ideas using the tools provided by the software.The dynamic notions (e.g., dynamic point, dynamic segment, instrumental decoding, hybrid-dynamic objects, active/ "alive" representations etc.), are taken as given and form the specific /particular theoretical basis for the constructive processes.Dynamic Euclidean constructions will be considered using pseudo-Toulmin' diagrams.These considerations provide a theoretical basis for the idea that, in order to solve a mathematical construction problem in Dynamic Euclidean Geometry, we have to build up the interdependencies of tools in various sequential steps (based on theorems and definitions and the competence in using tools) which can be linked to the level of our conceptualization.The central idea is the following: Do the tools of Dynamic Euclidean Geometry determine a new kind of Geometry?Is Dynamic Euclidean Geometry a new kind of geometry?Does it have its own axiomatic system or its own undefined terms?In the paper, the notion of an instrumental learning path/trajectory is introduced as the interdependence/intradependence between dynamic tools, diagrams and mathematical objects during an instrumental decoding process.
Testing Exchangeability With Martingale for Change-Point Detection

This work proposes a new exchangeability test for a random sequence through a martingale-based approach. Its main contributions include 1) an additive martingale which is more amenable for designing exchangeability tests by exploiting the Hoeffding-Azuma lemma and 2) different betting functions for constructing the additive martingale. By choosing the underlying probability density function of p-values as a betting function, it can be shown that, when a change-point appears, a satisfying trade-off between the smoothness and expected one-step increment of the martingale sequence can be obtained. An online algorithm based on beta distribution parametrization for constructing this betting function is discussed in detail as well.
Securing Cloud Computing Through IT Governance

Lack of alignment between information technology (IT) and the business is a problem facing many organizations. Most organizations, today, fundamentally depend on IT. When IT and the business are aligned in an organization, IT delivers what the business needs and the business is able to deliver what the market needs. IT has become a strategic function for most organizations, and it is imperative that IT and business are aligned. IT governance is one of the most powerful ways to achieve IT to business alignment. Furthermore, as the use of cloud computing for delivering IT functions becomes pervasive, organizations using cloud computing must effectively apply IT governance to it. While cloud computing presents tremendous opportunities, it comes with risks as well. Information security is one of the top risks in cloud computing. Thus, IT governance must be applied to cloud computing information security to help manage the risks associated with cloud computing information security. This study advances knowledge by extending IT governance to cloud computing and information security governance.
RETRACTED: Analysis of Application of Tennis Electronic Referee Based on Artificial Intelligence in Tennis Matches

Abstract In today’s era of rapid development of artificial intelligence, with the continuous development of technology, electronic referees have become an important part of many sports events, and artificial intelligence technology has become the brain of competitive sports and integrated into major events. this article uses artificial intelligence algorithms, graph theory and ID3 algorithms to establish an analysis of whether electronic referees violate rules in tennis matches. And use post-pruning technology to realize the pruning of the classification decision tree, generate classification rules, and complete the construction of the classification decision tree model used by the tennis electronic referee in the tennis match. The experimental results show that the application analysis of the artificial intelligence-based tennis electronic referees in tennis matches explored in this paper is more recognized and liked by athletes and spectators, and the athletes’ perception of the fairness of the game has increased by about 55%.
RETRACTED: Research on oil painting creation mode based on multimedia technology in big data Era

Abstract With the continuous development of social economy, people’s living standards have been significantly improved. Under the impact of the information age, it has caused an indelible impact on the industry. In today’s era, with the continuous promotion of the development of the field, the art industry also has a lot of innovation and improvement. Due to the continuous expansion and simplicity of multimedia technology, we have a better experience of the visual effect experience of daily life, and promote the development of the art industry. In today’s information society, data-based situation, now the painting creation is also welcome to its brand-new side, it is moving towards the direction of diversified development and constantly forward exploration. The diversified forms shown in the era of big data have turned the visual art of painting into a kind of data symbolization, which meets the requirements of the development of multimedia in today’s society and has an impact on today’s social painting art to a certain extent. In the era background of today’s big data society, it also has some significant impact on the thoughts of many artists, and then has an impact on the social art development.
Cervical cell TCT image detection and segmentation based on multi-scale feature fusion

Based on Mask R-CNN, this paper proposes a cervical image target detection and segmentation model based on multi-scale feature fusion. Mask R-CNN's backbone network-Feature Pyramid (FPN) uses top-down cross-layer connection, which causes the network to pay too much attention to the optimization of low-level features, resulting in a problem of reduced segmentation performance. Aiming at this problem, this paper uses NAS-FPN to determine the search space under the guiding ideology of classification network architecture search, realizes automatic cross-layer connection, and obtains the best feature fusion strategy. And use Soft NMS instead of NMS to reduce the mistaken deletion of bounding boxes and improve the accuracy of target detection. To The author produced his own TCT cell image data set (New Perdell's liquid-based cytological detection technique) under the guidance of medical laboratory personnel. After expanding the TCT data set, experiments show that the improved model in this paper can effectively improve the accuracy, recall and segmentation accuracy of epithelial cells, white blood cells, and fungi.
Is there a Security Mindset and Can it be Taught?

The field of cybersecurity is becoming very dynamic, and needs continuous evolution. This requires not only the formal and informal education, but a security mindset to be developed for our future workforce. This panel elaborates on some such aspects.
Application of an Artificial Neural Network to Evaluate the Integration Effect of Quality Education and Skill Education

Quality education is the basis, driver, and inspirer of skill education. These two education models can complement and interact with each other. However, few scholars have discussed the current state and future trend of the integration between the two models, not to mention quantifying the integration effect. This paper applied the artificial neural network (ANN) to evaluate the integration effect of quality education and skill education, owing to the advantages of the ANN in processing nonlinear information adaptively. First, the subjects and motivation mechanismss of the integration between the two models were analyzed. Then, an evaluation index system was established for the integration effect. After that, an ANN model was created for the compatibility of evaluation indexes and used to predict the integration effect. Experimental results verified the reasonability of the proposed evaluation index system, and the effectiveness of the proposed model. Finally, the current state of the integration was analyzed based on the prediction results.
Adult Content Detection on Arabic Twitter: Analysis and Experiments

With Twitter being one of the most popular social media platforms in the Arab region, it is not surprising to find accounts that post adult content in Arabic tweets; despite the fact that these platforms dissuade users from such content. In this paper, we present a dataset of Twitter accounts that post adult content. We perform an in-depth analysis of the nature of this data and contrast it with normal tweet content. Additionally, we present extensive experiments with traditional machine learning models, deep neural networks and contextual embeddings to identify such accounts. We show that from user information alone, we can identify such accounts with F1 score of 94.7% (macro average). With the addition of only one tweet as input, the F1 score rises to 96.8%.
Approach to hand posture recognition based on hand shape features for human–robot interaction

Abstract Hand segmentation is the initial step for hand posture recognition. To reduce the effect of variable illumination in hand segmentation step, a new CbCr-I component Gaussian mixture model (GMM) is proposed to detect the skin region. The hand region is selected as a region of interest from the image using the skin detection technique based on the presented CbCr-I component GMM and a new adaptive threshold. A new hand shape distribution feature described in polar coordinates is proposed to extract hand contour features to solve the false recognition problem in some shape-based methods and effectively recognize the hand posture in cases when different hand postures have the same number of outstretched fingers. A multiclass support vector machine classifier is utilized to recognize the hand posture. Experiments were carried out on our data set to verify the feasibility of the proposed method. The results showed the effectiveness of the proposed approach compared with other methods.
Probability Density Function Control for Stochastic Nonlinear Systems using Monte Carlo Simulation

This paper presents an implementable framework of output probability density function (PDF) control for a class of stochastic nonlinear systems which are subjected to non-Gaussian noises. The statistical properties of the system outputs can be adjusted by shaping the dynamic output probability density function to track the reference stochastic distribution. However, the dynamic probability density function evolution is very difficult to obtain analytically even if the system model and the stochastic distributions of the noises are known. Motivated by Monte Carlo simulation, the dynamic probability density function can be estimated by sampling data which forms the contribution of this paper. In particular, the sampling points are generated following the stochastic distribution of the noise for each instant. These points go through the system and generate the histogram for system outputs, then the dynamic model can be established based on the dynamic histogram which reflects the randomness and the nonlinear dynamics of the investigated system. Based on the established model, the output probability density function tracking can be achieved and the simulation results and discussions show the effectiveness and benefits of the presented framework.
Online Human Action Recognition Using Deep Learning for Indoor Smart Mobile Robots

This research proposes a vision-based online human action recognition system. This system uses deep learning methods to recognise human action under moving camera circumstances. The proposed system consists of five stages: human detection, human tracking, feature extraction, action classification and fusion. The system uses three kinds of input information: colour intensity, short-term dynamic information and skeletal joints. In the human detection stage, a two-dimensional (2D) pose estimator method is used to detect a human. In the human tracking stage, a deep SORT tracking method is used to track the human. In the feature extraction stage, three kinds of features, spatial, temporal and structural, are extracted to analyse human actions. In the action classification stage, three kinds of features of human actions are respectively classified by three kinds of long short-term memory (LSTM) classifiers. In the fusion stage, a fusion method is used to leverage the three output results from the LSTM classifiers. This study constructs a computer vision and image understanding (CVIU) Moving Camera Human Action dataset (CVIU dataset), containing 3,646 human action sequences, including 11 types of single human actions and 5 types of interactive human actions. This dataset was used to train and evaluate the proposed system. Experimental results showed that the recognition rates of spatial features, temporal features and structural features were 96.64%, 81.87% and 68.10%, respectively. Finally, the fusion result of human action recognition for indoor smart mobile robots in this study was 96.84%.
Semi-automatic Manga Colorization Using Conditional Adversarial Networks

Manga colorization is time-consuming and hard to automate. In this paper, we propose a conditional adversarial deep learning approach for semi-automatic manga images colorization. The system directly maps a tuple of grayscale manga page image and sparse color hint constructed by the user to an output colorization. High-quality colorization can be obtained in a fully automated way, and color hints allow users to revise the colorization of every panel independently. We collect a dataset of manually colorized and grayscale manga images for training and evaluation. To perform supervised learning, we construct synthesized monochrome images from colorized. Furthermore, we suggest a few steps to reduce the domain gap between synthetic and real data. Their influence is evaluated both quantitatively and qualitatively. Our method can achieve even better results by fine-tuning with a small number of grayscale manga images of a new style. The code is available at github.com.