Reviewerly/exHarmony huggingface dataset:
Application of Higher Order Ordinary Differential Equation Model in Financial Investment Stock Price Forecast

Abstract In order to improve the modelling efficiency in dynamic system prediction, this paper proposes a predictive model based on high-order normal differential equations to model high-order differential data to obtain an explicit model. The high-order constant differential equation model is reduced, and the numerical method is used to solve the predictive value. The results show that the method realises the synchronisation of model establishment and parameter optimisation, and greatly enhances the modelling efficiency.
Classifying Divergences in Cross-lingual AMR Pairs

Translation divergences are varied and widespread, challenging approaches that rely on parallel text.To annotate translation divergences, we propose a schema grounded in the Abstract Meaning Representation (AMR), a sentence-level semantic framework instantiated for a number of languages.By comparing parallel AMR graphs, we can identify specific points of divergence.Each divergence is labeled with both a type and a cause.We release a small corpus of annotated English-Spanish data, and analyze the annotations in our corpus.
A COMPARISON OF LYAPUNOV AND FUZZY APPROACHES TO TRACKING CONTROLLER DESIGN

There are numerous types of locomotion of mobile robots. Therein, the most widespread type of locomotion is motion using wheels. The task of robot is transport themselves from place to place. And tracking control is always an important problem to appply robots in practice. The robot has to reach the final goal by following a referenced trajectory. The paper proposes two methods based on the lyapunov stability standard and fuzzy law. Then, we simulate the algorithms to evaluate the results.
CREDIT CARD FRAUD DETECTION USING MACHINE LEARNING

Due to the rapid growth of the E-Commerce industry, the use of credit cards for online purchases has increased dramatically.In recent years, credit card fraud is becoming a major complication for banks as it has become very difficult for detecting fraud in the credit card system.To overcome this hardship Machine learning plays an eminent role in detecting the credit card fraud in the transactions.Modeling prior credit card transactions with data from ones that turned out to be fraudulent is part of the Card Fraud Detection Problem.In Machine learning the machine is trained at first to predict the output so, to predict the various bank transactions various machine learning algorithms are used.The SMOTE approach was employed to oversample the dataset because it was severely unbalanced.This paper the examines and overview the performance of K-nearest neighbors, Decision Tree, Logistic regression and Random forest, XGBoost for credit card fraud detection.The assignment is implemented in Python and uses five distinct machine learning classification techniques.The performance of the algorithm is evaluated by accuracy score, confusion matrix, f1-score, precision and recall score and auc-roc curve as well.
The Characteristics of Instructional Systematic Design English Online Teaching Environment

The English online learning instructional model became contingent upon organised and guided learning practices by competent English lecturers as instructors in the online situation. There are a few concepts applied to the English instructional online pedagogy for Higher Education and Universities. The purposes of the examinations are to classify the relevance of online instructional learning to conform to English learning and present current or unexpected instructional design into the English online environment. The study employed a convergent parallel design method. This method of choice had depth-analysing and performing parallel analyses, which are considered alternative evidence for discussing the components of the online instructional model. The examination consisted of 52 English lecturers from various Higher Education and Universities. The survey questionnaires were addressed to participants, and interview strategies were identified to collect a distinctive angle and dept-involvement on the English online instructional. Findings of the study have revealed that English lecturers considered learner-centered intended course as very important for their teaching concept while emphasising real-world performance as important for the online environment material. Collaborative approach and concentrate on reliably and validly assessed outcomes become the second phase importance in the online instructional model. Interview protocols thematic analysis exposed that English lecturers should support and organise their particular English online instructional online environment. The general characterised adapted to engage in English online instructional online-based. The condition is because of English lecturers empathising with the appropriate material, approach to the learners’ flexibility, and individual accomplishment in every topic.
Enriching and Clustering Short Text Using KNN

Semantic Hashing technique wraps the meaning of short texts into compressed binary codes.So, to find out that whether two short texts are alike or not in their meaning, their binary codes need to be matched.A deep neural network is used for encoding.Bag-of-words representation of texts is used to train the neural network.Unfortunately, the fundamental semantics are not sufficiently captured by the above mentioned form of representation for short texts such as titles, tweets, or queries.We propose adding additional semantic signals to better group short texts using their meaning.More specifically, we procure the co-occurring terms and concepts of every term in the short text via a knowledge database to further enhance the short text.Additionally, we use a k-Nearest Neighbor based approach id for hashing.Multiple experiments provide evidence that by increasing the number of semantic signals, our neural network is better capable to capture the meaning of short texts, which enables various uses like retrieving information, classifying data, and processing of short texts.
Augmented Reality sebagai Media Pendidikan Interaktif dalam Pandemi Covid-19

Abstract - In early 2020 various parts of the world announced a pandemic status due to the Covid-19 outbreak caused by the Corona virus. Indonesia is one of the countries affected by this epidemic. The public is urged to implement health procedures such as taking vitamins, basking in the sun, doing physical distancing, staying at home, working from home, Study from Home and so forth. Almost all sectors were paralyzed to minimize the spread of the covid-19 virus. Starting from the tourism sector, the economic sector and even the education sector. The Education Sector, which in its application must run classes in an amount that is not small, must also be limited. Academics and students in various regions in Indonesia are prohibited from traveling on business trips, out of town or even going to schools or educational institutions. One technology that has the potential to be used as an Education medium is Augmented Reality. In the past few years Augmented Reality has launched educational media products for Education in various sectors. In this journal, we will study about augmented reality-based Education media applications that can be used to study at home or Study from Home, so that it can improve physical distancing or reduce mass gathering to help the government in tackling the covid-19 pandemic.
On the Role of Context-Awareness in Binary Image Comparison

The quantification of image similarity has been a key topic in the computer vision literature for the past few years.Different mathematical theories have been used in the development of these measures, which we will refer to as comparison measures.An interesting aspect in the study of comparison measures is the natural requirement to replicate human behavior.In almost all cases, it is appropriate for a comparison measure to produce results that are consistent with how humans would perform that assessment.However, despite accepting this premise, most of the proposals in the literature ignore a fundamental characteristic of the way in which humans carry out this evaluation: the context of comparison.In this work we present a comparison measure for binary images that incorporates the context of comparison; more precisely, we introduce an approach for the generation of ultrametrics for the context-aware comparison of binary images.
Chapter 13. Generalizable Neuro-Symbolic Systems for Commonsense Question Answering

This chapter illustrates how suitable neuro-symbolic models for language understanding can enable domain generalizability and robustness in downstream tasks. Different methods for integrating neural language models and knowledge graphs are discussed. The situations in which this combination is most appropriate are characterized, including quantitative evaluation and qualitative error analysis on a variety of commonsense question answering benchmark datasets.
Scalable Cluster-Consistency Statistics for Robust Multi-Object Matching

We develop new statistics for robustly filtering corrupted keypoint matches in the structure from motion pipeline.The statistics are based on consistency constraints that arise within the clustered structure of the graph of keypoint matches.The statistics are designed to give smaller values to corrupted matches and than uncorrupted matches.These new statistics are combined with an iterative reweighting scheme to filter keypoints, which can then be fed into any standard structure from motion pipeline.This filtering method can be efficiently implemented and scaled to massive datasets as it only requires sparse matrix multiplication.We demonstrate the efficacy of this method on synthetic and real structure from motion datasets and show that it achieves state-ofthe-art accuracy and speed in these tasks.Our code is released at https://github.com/yunpeng-shi/FCC.
Arabic Sentiment Analysis for Multi-dialect Text using Machine Learning Techniques

Social media networks facilitated the availability and accessibility of a wide range of information and data.It allows the users to share and express their opinions.In addition, it presents the appraisals of the top news and the evaluation of movies, products, and services.This headway has been controlled by a well-known field called Sentiment Analysis (SA).Compared to the research studies conducted in English Sentiment Analysis (ESA), little effort is exerted in Arabic Sentiment Analysis (ASA).The Arabic language is a morphologically rich language that poses significant challenges to Natural Language Processing (NLP) systems.The purpose of the paper is to enrich the Arabic Sentiment Analysis via proposing a sentiment analysis model for analyzing an Arabic multi-dialect text using machine learning algorithms.The proposed model is applied to two datasets: ASTD Egyptian-Dialect tweets and RES Multi-Dialect restaurant reviews.Different evaluation measures were used to evaluate the proposed model to identify the best performing classifiers.The findings of this research revealed that the developed model outperformed the other two research works in terms of accuracy, precision, and recall.In addition, the Bernoulli Naive Bayes (B-NB) classifier achieved the best results with 82% for the ASTD Egyptian-Dialect tweets dataset, while the SVM classifier scored the best accuracy result for the RES Multi-Dialect reviews dataset with 87.7%.
A Survey on Adversarial Example

In recent years, deep learning has become a hotspot and core method in the field of machine learning. In the field of machine vision, deep learning has excellent performance in feature extraction and feature representation, making it widely used in directions such as self-driving cars and face recognition. Although deep learning can solve large-scale complex problems very well, the latest research shows that the deep learning network model is very vulnerable to the adversarial attack. Add a weak perturbation to the original input will lead to the wrong output of the neural network, but for the human eye, the difference between origin images and disturbed images is hardly to be notice. In this paper, we summarize the research of adversarial examples in the field of image processing. Firstly, we introduce the background and representative models of deep learning, then introduce the main methods of the generation of adversarial examples and how to defend against adversarial attack, finally, we put forward some thoughts and future prospects for adversarial examples.
A Hybrid Deep Neural Network for Human Activity Recognition based on IoT Sensors

Internet of things (IOT) sensors, has received a lot of interest in recent years due to the rise of application demands in domains like ubiquitous and context-aware computing, activity surveillance, ambient assistive living and more specifically in Human activity recognition.The recent development in deep learning allows to extract high-level features automatically, and eliminates the reliance on traditional machine learning techniques, which depended heavily on hand crafted features.In this paper, we introduce a network that can identify a variety of everyday human actions that can be carried out in a smart home environment, by using raw signals generated from Internet of Thing's motion sensors.We design our architecture basing on a combination of convolutional neural network (CNN) and Gated recurrent unit (GRU) layers.The CNN is first deployed to extract local and scale-invariance features, then the GRU layers are used to extract sequential temporal dependencies.We tested our model called (CNGRU) on three public datasets.It achieves an accuracy better or comparable to existing state of the art models.
Incorporating Cultural Design Elements in Mobile Applications Creative Industries in Malaysia: A Conceptual Study

In the creative industries sector, mobile applications are the creative multimedia division for advancing technology, the industry revolution 4.0 (IR 4.0), in developing creative products. The creative industries benefit from the usage of mobile applications since it allows for the digital manufacturing of creative products that meet the IR 4.0 era. However, research on the use of cultural design aspects in mobile applications has several limits. This research aims to talk about the conceptual framework for the research that will be done to tackle the issues that occur. According to preliminary findings, the design concepts analysed a lack of cultural design aspects. Therefore, the researcher will research to generate design guidelines for mobile applications for the creative industries that include cultural design elements.
New Insights into the Justifiability of Organizational Information Security Policy Noncompliance: A Case Study

Information security policies as apparatus for communicating security principles with employees are the cornerstone of organizational information security.Resultantly, extant literature has looked at different theories to better understand the noncompliance problem.Neutralization theory is emerging as one of the most popular approaches, not only as an explanation but also as a solution.In this in-depth qualitative study, we ask the question 'how do employees justify violating the ISP'?Our findings reveal nine rationalizing techniques, three of which have not been recognized in previous research.We label them 'I follow my own rules', 'matter of mere legality' and 'defense of uniqueness'.But more importantly, our in-depth insights point to the danger of taking these rationalizations out of context, since without context, it becomes impossible to judge whether the behavior or the rule, needs correcting, reflecting a dilemma recognized in the original writing of neutralization theory, which has since been forgotten.
Automated Telugu Printed and Handwritten Character Recognition in Single Image using Aquila Optimizer based Deep Learning Model

Machine printed or handwritten character recognition becomes a major research topic in several real time applications.The recent advancements of deep learning and image processing techniques can be employed for printed and handwritten character recognition.Telugu character Recognition (TCR) remains a difficult task in optical character recognition (OCR), which transforms the printed and handwritten characters into respective text formats.In this aspect, this study introduces an effective deep learning based TCR model for printed and handwritten characters (DLTCR-PHWC).The proposed DLTCR-PHWC technique aims to detect and recognize the printed as well as handwritten characters that exist in the same image.Primarily, image pre-processing is performed using the adaptive fuzzy filtering technique.Next, line and character segmentation processes are performed to derive useful regions.In addition, the fusion of EfficientNet and CapsuleNet models is used for feature extraction.Finally, the Aquila optimizer (AO) with bi-directional long short-term memory (BiLSTM) model is utilized for recognition process.A detailed experimentation of the proposed DLTCR-PHWC technique is investigated using Telugu character dataset and the simulation outcome portrayed the supremacy of the proposed DLTCR-PHWC technique over the recent state of art approaches.
Blind Image Quality Assessment of Screen Content Images via Fisher Vector Coding

This paper presents an effective blind image quality assessment (BIQA) method for screen content images (SCIs) based on Fisher vector encoding, with the hypothesis that local statistics will be altered with the change of distortions, and can be characterized by the fusion of statistical models and direction vectors. Firstly, a specific Gaussian mixture model (GMM) is generated from a corpus of pristine SCIs to simulate the local distribution of SCIs in spatial domain. Then, discriminative features are generated to characterize the quality of test image with Fisher vector coding and generated GMM. Finally, support vector regression is adopted to learn the mapping between discriminative features and subjective opinion scores. To validate the performance of our method, extensive experiments are conducted on three public SCI databases and the results well confirm its superiority over the existing relevant BIQA method of SCIs.
Improvement of Searching for Appropriate Textual Information Sources Using Association Rules and FCA

This paper deals with an optimization of methods for recommending relevant text sources. We summarize methods that are based on a theory of Association Rules and Formal Conceptual Analysis which are computationally demanding. Therefore we are applying the ‘Iceberg Concepts’, which significantly prune output data space and thus accelerate the whole process of the calculation. Association Rules and the Relevant Ordering, which is an FCA-based method, are applied on data obtained from explications of an atomic concept. Explications are procured from natural language sentences formalized into TIL constructions and processed by a machine learning algorithm. TIL constructions are utilized only as a specification language and they are described in numerous publications, so we do not deal with TIL in this paper.
Social Media Usage for Learning English Language

The reason of this consider was to decide the sorts of social media utilized by fourth-semester students of the Department of English Literature, Universitas Negeri Medan, for learning English. The analyst utilized clear subjective inquire about, and the information were collected using a questionnaire. The participants were 50 students of the Department of English Letters, Muria Kudus University. The results showed that the social media used by students to learn English were YouTube (94%), Instagram (72%), WhatsApp (62%), Twitter (32), Facebook (30%), Line (10%), Google (2%), TikTok (2%).
No-Go Theorems for Distributive Laws

Monads are commonplace in computer science, and can be composed using Beck's distributive laws. Unfortunately, finding distributive laws can be extremely difficult and error-prone. The literature contains some general principles for constructing distributive laws. However, until now there have been no such techniques for establishing when no distributive law exists. We present three families of theorems for showing when there can be no distributive law between two monads. The first widely generalizes a counterexample attributed to Plotkin. It covers all the previous known no-go results for specific pairs of monads, and includes many new results. The second and third families are entirely novel, encompassing various new practical situations. For example, they negatively resolve the open question of whether the list monad distributes over itself, reveal a previously unobserved error in the literature, and confirm a conjecture made by Beck himself in his first paper on distributive laws. In addition, we establish conditions under which there can be at most one possible distributive law between two monads, proving various known distributive laws to be unique.
Meaningful Learning Model: The Impact on Students’ Reading Comprehension

The purpose of this study is examining the impact of meaningful learning model on students’ reading comprehension. This study uses true experimental research with pretest-posttest control group design. The class is divided into two classes; experimental class who receives treatments by using meaningful learning model and control class who receives treatments by using conventional teaching model. The total of sample is 110 students who are selected from eight grade students of junior high schools either state or private schools in Central Java Province. To obtain valid data, this study uses tests given at the pre-test and post-test in both classes. After the data is obtained, the data is analyzed by using the SPSS t-test formula. Based on the results of the calculations in the pretest and posttest, it was known that both classes are homogeneous and normally distributed from the calculation of the Levene’s test method and Saphiro-Wilk. In the pretest, t-count (0.452) is lower than t-table (1.982), whereas in the posttest, t-count (2.361) is higher than t-table (1.982). This indicates that meaningful learning model has an influence on student grades in reading comprehension rather than using conventional teaching model.&#x0D; &#x0D; Received: 4 October 2021 / Accepted: 17 November 2021 / Published: 3 January 2022