Reviewerly/exHarmony huggingface dataset:
Data Level Approach for Multiclass Imbalance Financial Data

In the real world, the class imbalance problem is a common issue in which classifier gives more importance to the majority class whereas less importance to the minority class. In class imbalance, imbalance metrics would not be suitable to evaluate the performance of classifiers with error rate or predictive accuracy. One type of imbalance data -handling method is resampling. In this paper, three resampling methods, oversampling, under-sampling and hybrid, methods are used with different approaches for in class imbalance of two different financial data to see the impact of class imbalance ratios on performance measures of nine different classification algorithms. Aiming to achieve better change classification performance, the performance of the classification algorithms, Bayes Net, Navie Bayes, J48, Random Forest Meta-Attribute Selected Classifier, MetaClassification via Regression, Meta-Logitboost, Logistic Regression, and Decision Tree, are measured on two Canadian Banks multiclass imbalance data with the performance measures, Precision, Recall, ROC Area and Kappa Statistic, by using WEKA software. The outcome of these performance measurements compared with three different resampling methods. The results provide us with a clear picture on the overall impact of class imbalance on the classification dataset and they indicate that proposed resampling methods can also be used for in class imbalance problems
Cloud detection of multi-feature remote sensing images based on deep learning

Abstract Satellite remote sensing technology provides massive image data for meteorological field, which can accurately extract the spatial distribution of cloud, and can be used to analyze the spatial and temporal changes of cloud. Aiming at the problems that the traditional algorithm is sensitive to noise and has poor recognition effect on broken clouds, a multi-feature remote sensing image cloud detection algorithm based on neural network structure is proposed, which has a good recognition effect on all types of clouds. The experimental results show that the accuracy of cloud detection in this paper reaches 92%, which is about 6% higher than the traditional algorithm, and avoids the disadvantage of the traditional algorithm which is sensitive to noise.
Bilingual Lexicon Approach to English-Filipino Sentiment Analysis of Teaching Performance

Abstract The aim of this study is to formulate a strategy that can possibly calculate teacher performance by analyzing textual feedback. Expressing textual responses in quantitative form like average sentiment rating can actually provide opportunities for administrators to see if the numerical ratings given complement that of the comments. Our approach was designed to enable processing bilingual textual data. Findings of this study shows that there is strong correlation between teaching performance actual mean rating and average sentiment rating. Furthermore, the approach employed obtained 86% accuracy indicating that it is an encouraging technique, capable of analyzing the students' textual responses. In future work, the use of POS tagging can be explored to improve sentiment analysis accuracy. Employing machine learning methods may also be considered to discover techniques and alternative approaches to sentiment classification.
Dances with Robots

What does it feel like to dance with a robot? How do you choreograph one? Working with robots during three artistic residencies and two research projects has raised questions about agency and generative processes, revealing how dancing with robots may provoke a more interanimate everyday world.
Enhancing firefly algorithm with multiple swarm strategy

Firefly algorithm (FA) is one of most important nature-inspired algorithm based on swarm intelligence. Meanwhile, FA uses the full attraction model, which results too many unnecessary movements and reduces the efficiency of searching the optimal solution. To overcome these problems, this paper presents a new job, how the better fireflies move, which is always ignored. The novel algorithm is called multiple swarm strategy firefly algorithm (MSFFA), in which multiple swarm attraction model and status adaptively switch approach are proposed. It is characterized by employing the multiple swarm attraction model, which not only improves the efficiency of searching the optimal solution, but also quickly finds the better fireflies that move in free status. In addition, the novel approach defines that the fireflies followed different rules in different status, and can adaptively switch the status of fireflies between the original status and the free status to balance the exploration and the exploitation. To verify the robustness of MSFFA, it is compared with other improved FA variants on CEC2013. In one case of 30 dimension on 28 test functions, the proposed algorithm is significantly better than FA, DFA, PaFA, MFA, NaFA,and NSRaFA on 24, 23, 23, 17, 15, and 24 functions, respectively. The experimental results prove that MSFFA has obvious advantages over other FA variants.
Construction of Intelligent Adaptive Learning Platform in Ubiquitous Environment

With the development of AI, 5G and big data technology, learning mode has gradually changed from traditional learning to ubiquitous learning (U-learning), which is a new learning paradigm that has been studied in recent years all over the world. Fully applying ubiquitous computing technology and building an intelligent online learning education platform to provide learners with powerful learning experience are the future development trend of intelligent learning. The concept of intelligent adaptive learning platform (IALP) is proposed based on the analysis of the shortcomings of existing platforms from a ubiquitous perspective. To sort out and outline the system architecture features and application functions of the intelligent adaptive learning platform can provide strategic thinking for relevant researchers to further clarify the implementation path of the intelligent adaptive learning platform. In the future, intelligent adaptive learning platform will play its advantages in precise learning, precise recommendation, precise evaluation and precise counseling, which will contribute to the current development of education informatization and intellectualization.
Fitness for Solving SMCP Using Evolutionary Algorithm

Abstract Search based software engineering is a paradigm with prime focus to apply search techniques and meta-heuristics for solving various software engineering open NP hard problems. Evolutionary meta-heuristics techniques are already proven to provide optimized solutions to other software engineering problems like automated software test data generation, project estimation, class responsibility assignment to name few. Software module clustering is such an open problem of software engineering that cannot be solved in definite manner. For a given module dependency graph of software system, there exists large number of possible partitions. Identifying a good partition to cluster all modules of software system is an exhaustive search that cannot be carried out in finite manner. With search based techniques applying evolutionary algorithms, an optimized solution can be identified with evaluating goodness of a given partitions. Identifying a fitness function that can direct the search towards optimal solution is very critical. This paper discusses various types of fitness applicable to solve software module clustering problem.
Show Auto-Adaptive and Tell: Learned From the SEM Image Challenge

Scanning electron microscopy (SEM) has been widely used in optical material science. However, a considerable quantity of human resources is required to analyze and describe SEM images. In recent years, the application of computer technology in material science and engineering developed endlessly. Computer science, including data processing, simulation technique, and mathematical model, promotes material science progress tremendously. Moreover, deep learning has been achieved success in image classification and image analysis. In this paper, we propose a novel automatic analysis tool using a triplet neural network called show auto-adaptive and tell to analyze optical SEM images automatically. Firstly, we collected SEM images and corresponding captioning from previous papers and built a database. Then, a triplet neural network with proposed loss function to train the show auto-adaptive and tell model on 60% of the dataset for SEM images analysis, test on 30% and validate on 10%. Finally, experiment on the four metrics index as the evaluation criterion shows that the novel method gets better performance than previous work.
Anomaly and Degradation Detection Using Subspace Tracking in Streaming Data

In our increasingly connected digital world, many sensors are connected to each other. Each sensor provides several features and data from those features. As a result, the data sets created from Internet of Things (IoT) applications can consist of hundreds or even thousands of dimensions. Even though the dimensionality is high in these data sets, the actual rank is mostly low because of the high correlations between these dimensions. As a result, subspace analysis and subspace tracking are useful methods of capturing low-dimensional structures from high-frequency, high-dimensional data. In streaming data, a substantial change in the subspace can indicate an anomaly. In this paper, we introduce several measures for detecting changes in the subspace that we use in our anomaly and degradation detection methods. We also present the results of applying our methods to simulated and real-world data sets. These methods have been implemented in an event stream processing platform.
Elevating "Everyday" Voices and People in Archives through the Application of Graph Database Technology

In a simple experiment using a graph database we demonstrate that it is possible to increase the number of access points to individual items in archival collections. We do this by leveraging existing machine readable and searchable data and metadata to identify and display relationships between persons, places, dates, events, etc. across items and collections. We discuss some of the financial, ethical and representational implications of decisions made in applying technology to archival holdings. Many decisions are made without considering the ethical and representational implications. Our experiment has illustrated some of these ethical and representational implications.
BiGRU-Multi-Head Self-Attention Network for Chinese sentiment classification

Abstract The rapid development of Internet and the extensive user base have led to a significant increase in the propagation speed of information and public opinion. Quick and accurate sentiment analysis of public opinion can help analyze and manage public opinion. Since BiGRU can simplify network parameters, and the Multi-Head Self-Attention(MHSA) mechanism can learn long-distance features more accurately and quickly. In this study, we propose a BiGRU-MHSA network to perform Chinese sentiment classification. The innovation of the model lies in the combination of BiGRU and MHSA in order to focus on several semantic centers and comprehend the whole text more precisely and efficiently. At the same time, we use the Chinese comment datasets on Weibo to verify the effectiveness and accuracy of the model. From the experimental results, we find that our proposed model improves the accuracy by about 1% on different comments datasets without consuming too much computing resources.
Swarm Intelligence-based Decision Trees Induction for Classification — A Brief Analysis

Decision trees are popular machine learning classifiers, they accurately represent the data in a simple manner that closely resembles human reasoning. Since inducing the optimal decision tree is a NP-hard problem, numerous traditional heuristic-based approaches were introduced to tackle it. However, due to the present data explosion, these greedy local methods did not guarantee the induction of an optimal tree. To address this issue, swarm intelligence algorithms have been currently applied to navigate the search space more appropriately, seeking optimal decision trees. The aim of this research study is to give an analysis overview of the most up-to-date existing swarm-based decision trees induction techniques in a shape of a comparative study, where we discuss the different basics, features, characteristics and results. This survey will serve as a guide for the researches community. However, due to the present data explosion, these greedy local methods did not guarantee the induction of an optimal tree. To address this issue, swarm intelligence algorithms have been currently applied to navigate the search space more appropriately, seeking optimal decision trees. The aim of this research study is to give an analysis overview of the most up-to-date existing swarm-based decision trees induction techniques in a shape of a comparative study, where we discuss the different basics, features, characteristics and results. This survey will serve as a guide for the researches community. The aim of this research study is to give an analysis overview of the most up-to-date existing swarm-based decision trees induction techniques in a shape of a comparative study, where we discuss the different basics, features, characteristics and results. This survey will serve as a guide for the researches community.
Developing Readability Computational Formula for Arabic Reading Materials Among Non-native Students in Malaysia

Efficient measures on the readability of Arabic reading materials are important in ensuring quality text to be used. However, there are limited tools in assessing readability of Arabic reading materials. Thus, the lack of measuring tools to assess the suitability of textbooks amongst non-native speakers is a constraint which led to this study. In this regard, this paper discusses the efforts to develop a readability formula for Arabic reading materials in Malaysia. This study involved 313 samples of population from thirteen Arabic textbooks used in religious secondary schools in Malaysia. A linguistic analysis is performed as well as an inference analysis to see the strength of the relationship of each linguistic pattern with the level of readability. The findings show that the relationship between the linguistic features that is being analysed is not strong. But this finding can be used as the basic formula for improvement. This is because the textbooks that have been sampled do not have good writing features. Thus, a comprehensive effort is needed to ensure the preparation of Arabic language textbook is of good quality for non-native students.
A Survey on “Object Detection Algorithms for Visually Impaired People”

Vision is one in every of the foremost necessary human senses and it plays a really necessary role in human interaction with the surrounding objects. Until now many papers have been published on these topics that shows various different computer vision products and services by developing new electronic devices for the visually disabled people. The aim is to study different object detection methods. As compared to other Object detection methods, YOLO method has multiple advantages. In alternative algorithms like CNN, Fast-CNN the algorithmic program won't investigate the image fully however in YOLO the algorithmic program investigate the image fully by predicting the bounding boxes by making use of convolutional network and possibilities for these boxes and detects the image quicker as compared to alternative algorithms.
Adaptive Image Scaling for Corresponding Points Matching between Images with Differing Spatial Resolutions

In this study, an image scaling method to improve the accuracy of the image registration between images using different imaging devices is proposed. It is known that conventional keypoint detection, description, and matching methods do not work well between images with different spatial resolutions, such as those captured by drones and satellites. Thus, we propose a method to improve the geometric accuracy of image registration through an adaptive combination of super-resolution and low- resolution images and downscaling to high-resolution images based on the assumption that artificial structures are perceived as relatively simple shapes in top-view images. If the superresolution factor is too high, artifacts are generated, and the accuracy of the corresponding matching points will be decreased. Thus, estimating the highest super-resolution factor while avoiding the artifacts is necessary. Using the super-resolution factor, super-resolution processing of satellite images and downscaling to drone images are simultaneously performed. This is followed by corresponding points matching to achieve high estimation accuracy in the image registration process. Through quantitative evaluation experiments performed using pairs of images with 12 times difference in spatial resolutions, we demonstrated that high-accuracy image registration is possible by applying super-resolution processing at a factor of 4 to 6.
A Hybrid Machine Learning Framework of Gradient Boosting Decision Tree and Sequence Model for Predicting Escalation in Customer Support

Large software organizations spend millions of dollars on their technical support system to resolve their users' concerns, provide excellent quality of support, and reduce friction from their systems. Sometimes situations arise where the expectation of customer conflict with the support team process, which may lead to the escalation of support cases. An escalation causes a lot of problems for the company and they try every possible solution to avoid escalation. Machine learning solutions help organizations to provide a seamless experience to their customers and help them to remain competitive in the market. This paper aims to predict whether a particular support case will be escalated by a customer in the nearest future based on past escalation cases, metadata recorded when the case opened, and information generated through the conversation customers have on their support ticket with the company representatives. We have developed a hybrid modelling framework of XGBoost (gradient boosting) and LSMT (sequence) model to effectively use sequential textual information, time-series data, and metadata for modelling. The modelling framework is generic and can be extended to any problem where both metadata (non time-series) and time-series available. For model interpretation and understanding of the main drivers behind escalation, we used global and local feature interpretability of the SHapley Additive exPlanations (SHAP) approach. This paper is a response to the IEEE BigData 2020 Cup challenge.
The overview of software and information support services for distance learning

The article provides the overview of software and services for information support of distance learning - e-learning systems, knowledge test control and video conferencing. The advantages of openness of software code and modularity of their structure are shown. The pros and cons of the installed software and cloud services by subscription are discussed. The systems are compared on a number of criteria. Examples of their practical application are also described.
DSD - Dynamic Stack Decider

Abstract We present the Dynamic Stack Decider (DSD), a lightweight open-source control architecture. It combines different well-known approaches and is inspired by behavior trees as well as hierarchical state machines . The DSD allows to design and structure complex behavior of robots as well as software agents while providing easy maintainability. Challenges that often occur in robotics, i.e., a dynamic environment and situation uncertainty, remain well-manageable. Furthermore, it allows fast modifications of the control flow, while providing the state-fullness of a state machine. The approach allows developing software using a simple Domain Specific Language (DSL) which defines the control flow and two types of elements that contain the programmed parts. The framework takes care of executing the demanded portions of the code and gives, due to its stack-like internal representation, the ability to verify preconditions while maintaining a clear structure. The presented software was used in different robotic scenarios and showed great performance in terms of flexibility and structuredness.
Entanglement generation and protection for two atoms in the presence of two parallel mirrors

Abstract We study, in the framework of open quantum systems, the entanglement generation of two atoms in between two parallel mirrors in a thermal bath of quantum scalar fields. We find that the presence of mirrors plays an important role in entanglement generation and protection. The entanglement dynamics is crucially dependent on the geometric configurations of the two-atom system with respect to the mirrors, and the ranges of temperature and interatomic separation within which entanglement can be generated are significantly changed compared with those in a free space. In particular, when the atomic transition wavelength is larger than twice the distance between the two mirrors, the atoms behave as if they were isolated from the environment and the entanglement can persist in the steady state if the atoms are initially entangled and no entanglement can be created if they are initially separable, no matter how the atoms are placed with respect to the mirrors and to each other. This is in sharp contrast to the fact that in a free space, steady-state entanglement is possible only when the two atoms are placed extremely close to each other, while in the presence of one mirror, it is possible when the two atoms placed extremely close to the mirror.
German News Article Classification: A Multichannel CNN Approach

Nowadays, more and more people are gaining interest in news and social media networks, and are also sharing their opinions freely in different languages. Such kind of activities leads to interesting topics of research that scientists are working on. Considering news, it must be classified and easily accessible by the users for the information of their interest. In comparison with traditional machine learning techniques, deep learning approaches have achieved surpassing results on natural language processing tasks. Convolutions neural networks (CNNs) have shown promising performance, which extracts n-grams as features to represent the input. In this work, we build a multi-channel CNN for German news article classification. The model can classify different categories of news articles with an accuracy of 99.2% on training and 81.4% on the test dataset. We also perform a comparative study with single-channel CNN and have found that the multi-channel approach outperforms the single-channel by +6.3% absolute on the test set.
Connotation, Characteristics, Function and Realization of Ubiquitous Learning

The progress of science and technology has brought profound influence to education, which makes human learning more convenient and effective. Ubiquitous computing technology makes ubiquitous learning (U-learning) possible. Under the ubiquitous environment, the learning style has gradually changed from traditional learning to U-learning. U-learning is a new learning paradigm which has been studied in China in recent years. The basic concepts of ubiquitous computing and ubiquitous learning are introduced, the connotation, characteristics and function of ubiquitous learning are discussed, and the technical realization approaches of ubiquitous learning are given.