Reviewerly/exHarmony huggingface dataset:
New binary archimedes optimization algorithm and its application

Optimization problem, as a hot research field, is applied to many industries in the real world. Due to the complexity of different search spaces, metaheuristic optimization algorithms are proposed to solve this problem. As a recently introduced optimization method inspired by physics, Archimedes Optimization Algorithm (AOA) is an efficient metaheuristic algorithm based on Archimedes' law. It has the advantages of fast convergence speed and balance between local and global search ability when solving continuous problems. However, discrete problems exist more in practical applications. AOA needs to be further improved in dealing with such problems. On this basis, to make Archimedes Optimization Algorithm better applied to solve discrete problems, a Binary Archimedes Optimization Algorithm (BAOA) is proposed in this paper, which incorporates a novel V-shaped transfer function. The proposed method applies the BAOA to COVID-19 classification of medical data, segmentation of real brain lesion, and the knapsack problem. The experimental results show that the proposed BAOA can solve the discrete problem well.
Machine Learning Approach to Analyze the Sentiment of Airline Passengers’ Tweets

As one of the most extensive social networking services, Twitter has more than 300 million active users as of 2022. Among its many functions, Twitter is now one of the go-to platforms for consumers to share their opinions about products or experiences, including flight services provided by commercial airlines. Using a machine learning approach, this study aimed to measure customer satisfaction by analyzing sentiments of tweets that mention airlines. Relevant tweets were retrieved from Twitter’s application programming interface and processed through tokenization and vectorization. After that, these processed vectors were passed into a pretrained machine learning classifier to predict the sentiments. In addition to sentiment analysis, we also performed a lexical analysis on the collected tweets to model keyword frequencies, which provided meaningful context to facilitate interpretation of the sentiments. We then applied time series methods such as Bollinger Bands to detect abnormalities in the sentiment data. Using historical records from January to July 2022, our approach was proven capable of capturing sudden and significant changes in passenger sentiments through the analysis of breakout points on the Bollinger upper and lower bounds. The methodology devised for this study has the potential to be developed into an application that could help airlines, along with other customer-facing businesses, efficiently detect abrupt changes in customer sentiments and consequently take appropriate mitigatory measures.
The recognition of speech defects using convolutional neural network

The paper proposes a solution to improve the efficiency of recognition of speech defects in children by processing the sound data of the spectrogram based on convolutional neural network models. For a successful existence in society, a person needs the most important skill - the ability to communicate with other people. The main part of the information a person transmits through speech. The normal development of children necessarily includes the mastery of coherent speech. Speech is not an innate skill for people, and children learn it on their own. Speech defects can cause the development of complexes in a child. Therefore, it is very important to eliminate them at an early age. So, the problem of determining speech defects in children today is a very urgent problem for parents, speech therapists and psychologists. Modern information technologies can help in solving this problem. The paper provides an analysis of the literature, which showed that models of CNN can be successfully used for this. But the results that are available today have not been applied to speech in Ukrainian. Therefore, it is important to develop and study models and methods of convolutional neural networks to identify violations in the speech of children. The paper describes a mathematical model of oral speech disorders in children, the structure of a convolutional neural network and the results of experiments. The results obtained in the work allow to establish one of the speech defects: dyslexia, stuttering, difsonia or dyslalia with recognition results of 77-79%.
Modelling of AGM-style doxastic operations in three-valued setting

The goal of our work is to show how a theoretical approach to modeling of reasoning can be analyzed to identify controversial issues that reveal prospects for further research. We will consider one of the basic approaches to modeling of reasoning based on the concept of belief revision AGM, which is viewed as classical because it formulates the basic concepts of belief, introduces the main ways of representing beliefs, cognitive actions, systems of postulates for cognitive actions and the basic principles for constructing epistemic systems. However, this conceptual foundation raises many controversial issues that require further research, such as the problem of purity of the doxastic operations, the problem of primacy of the doxastic operations and the problem of connection between the doxastic operations. To find a possible solution to these controversial points, we will attempt to model the main ideas of AGM within the framework of standard consistent, and complete logic \L{}3. The basic principle of our translation is the scheme for constructing an epistemic theory proposed by G\"ardenfors, which is considered the basis of AGM. We use a strict three-valued logic formalism to constrain the functioning of doxastic operators and to test how they will function when trying to express the corresponding AGM postulates in a given system. It will allow us to approach the solution of the classical AGM problems or at least to present them from a different perspective. We consider the fundamental possibility of obtaining other doxastic operators in this way and also show how we can implement the minimality criterion for the contraction operator by combining several theorems of three-valued logic. The presented method of translating an informal conceptual scheme into formal logic is convenient for teaching students the basics of modeling and makes it possible to demonstrate the relationships and limitations of the modeled objects and processes.
Enhancing out-of-class independent learning in a cloud-based information and communication learning environment: insights from students of a pedagogical university

This paper addresses the challenges associated with students’ out-of-class independent work in an information and communication learning environment that relies on cloud technologies. The study utilizes survey data gathered from students at a pedagogical university to inform the development of a course titled “Educational Electronic Resources for Primary School” designed for future primary school teachers. The findings reveal several prominent issues, including the need for more explicit instructions tailored to the task requirements, limited experience in self-management, and a lack of intrinsic motivation. Students emphasized the importance of detailed instructions, either orally or in written form, and emphasized the necessity of careful time planning to ensure successful completion of tasks. Furthermore, the article discusses students’ learning activities and achievements in the course amidst the challenges posed by the COVID-19 pandemic. Drawing from this analysis, the study formulates key requirements for effectively managing students’ out-of-class independent work in a cloud-based learning environment. This research contributes valuable insights to improving the design and implementation of remote learning initiatives, enhancing student engagement, and fostering meaningful learning outcomes.
Ukraine higher education based on data-driven decision making (DDDM)

This article presents a theoretical review and empirical study of the assessment of the quality of BA and MA online education during the Russian military invasion of Ukraine in 2022. A qualitative theoretical analysis and comparison are made with a study of higher education quality assessment across Ukraine during the war in 2022 (\textit{N} = 12019). The article analyzes the tools and structure of student feedback on the evaluation of the educational process. The undergraduate and graduate programs are modified based on the analysis of this data. In the Ukrainian higher education space, the National Qualifications Framework requirements for each specialty have been under development since 2014. The National Qualifications Framework developed a list of interdisciplinary competencies that should be transferred to academic courses. According to the Ukrainian higher education standard, universities must provide empirical evidence that their students actually acquire these competencies. Baseline data on the assessment of the quality of online education by student teachers during the war in Ukraine is presented using the case study of the Kriviy Rih State Pedagogical University (\textit{N} = 688). Further modifications of the questionnaire to assess the quality of teaching in order to improve data-driven decision-making and testing ethics are proposed.
This is not new! Spotting previously-verified claims over Twitter

Several fake claims are commonly repeated over time, especially on social media. To identify such previous claims, the verified claim retrieval task was studied, where, for a given input claim, the goal is to find previously-verified claims that are relevant to it. However, this view assumes that each claim was already verified, which may not be true for all claims in the real-world scenario. In this work, we introduce the Verified Claim Checking problem over Twitter, in which the relevant verified claims are retrieved only if the input claim was indeed previously-verified, thus saving computation time. We address the problem by proposing SpotVC, an end-to-end approach consisting of two stages, namely a filter and a reranker. The proposed filter achieved an average F1 of 0.81 while significantly reducing computation time. Moreover, the proposed reranker outperformed the state-of-the-art models on two public datasets and provided on-par performance on a third one. Overall, our proposed system exhibits an effective operational balance in the trade-off between efficiency and effectiveness for the real-world scenario.
REVEL Framework to Measure Local Linear Explanations for Black-Box Models: Deep Learning Image Classification Case Study

Explainable artificial intelligence is proposed to provide explanations for reasoning performed by artificial intelligence. There is no consensus on how to evaluate the quality of these explanations, since even the definition of explanation itself is not clear in the literature. In particular, for the widely known local linear explanations, there are qualitative proposals for the evaluation of explanations, although they suffer from theoretical inconsistencies. The case of image is even more problematic, where a visual explanation seems to explain a decision while detecting edges is what it really does. There are a large number of metrics in the literature specialized in quantitatively measuring different qualitative aspects, so we should be able to develop metrics capable of measuring in a robust and correct way the desirable aspects of the explanations. Some previous papers have attempted to develop new measures for this purpose. However, these measures suffer from lack of objectivity or lack of mathematical consistency, such as saturation or lack of smoothness. In this paper, we propose a procedure called REVEL to evaluate different aspects concerning the quality of explanations with a theoretically coherent development which do not have the problems of the previous measures. This procedure has several advances in the state of the art: it standardizes the concepts of explanation and develops a series of metrics not only to be able to compare between them but also to obtain absolute information regarding the explanation itself. The experiments have been carried out on four image datasets as benchmark where we show REVEL’s descriptive and analytical power.
Resampling approach for one-Class classification

The performance of a classification model depends significantly on the degree to which the support of each data class overlaps. Successfully distinguishing between classes is difficult if the support is similar. In the one-class classification (OCC) problem, wherein the data comprise only a single class, the classifier performance is significantly degraded if the population support of each class is similar. In this study, we propose a resampling algorithm that enhances classifier performance by utilizing the macro information that is most easily obtainable in these two problem situations. The algorithm aims to improve classifier performance by reprocessing the given data into data with mitigated class imbalance through raking and sampling techniques. This performance improvement is demonstrated by comparing representative classifiers used in the existing OCC problem with traditional binary classifier models, which are unavailable on a single-class dataset.
Learning Strategies for Sensitive Content Detection

Currently, the volume of sensitive content on the Internet, such as pornography and child pornography, and the amount of time that people spend online (especially children) have led to an increase in the distribution of such content (e.g., images of children being sexually abused, real-time videos of such abuse, grooming activities, etc.). It is therefore essential to have effective IT tools that automate the detection and blocking of this type of material, as manual filtering of huge volumes of data is practically impossible. The goal of this study is to carry out a comprehensive review of different learning strategies for the detection of sensitive content available in the literature, from the most conventional techniques to the most cutting-edge deep learning algorithms, highlighting the strengths and weaknesses of each, as well as the datasets used. The performance and scalability of the different strategies proposed in this work depend on the heterogeneity of the dataset, the feature extraction techniques (hashes, visual, audio, etc.) and the learning algorithms. Finally, new lines of research in sensitive-content detection are presented.
Automatic Essay Scoring Method Based on Multi-Scale Features

Essays are a pivotal component of conventional exams; accurately, efficiently, and effectively grading them is a significant challenge for educators. Automated essay scoring (AES) is a complex task that utilizes computer technology to assist teachers in scoring. Traditional AES techniques only focus on shallow linguistic features based on the grading criteria, ignoring the influence of deep semantic features. The AES model based on deep neural networks (DNN) can eliminate the need for feature engineering and achieve better accuracy. In addition, the DNN-AES model combining different scales of essays has recently achieved excellent results. However, it has the following problems: (1) It mainly extracts sentence-scale features manually and cannot be fine-tuned for specific tasks. (2) It does not consider the shallow linguistic features that the DNN-AES cannot extract. (3) It does not contain the relevance between the essay and the corresponding prompt. To solve these problems, we propose an AES method based on multi-scale features. Specifically, we utilize Sentence-BERT (SBERT) to vectorize sentences and connect them to the DNN-AES model. Furthermore, the typical shallow linguistic features and prompt-related features are integrated into the distributed features of the essay. The experimental results show that the Quadratic Weighted Kappa of our proposed method on the Kaggle ASAP competition dataset reaches 79.3%, verifying the efficacy of the extended method in the AES task.
Using Augmented Reality to Simulate Authentic Learning Building Assessment and Construction Experiences

As educators in Architecture, Engineering, and Construction (AEC) domains seek to better prepare graduates for industry, providing authentic, hands-on design and construction experiences has been found to be one of the most effective ways of achieving the goal of educators. However, providing access to these experiences is costly, time-consuming, and resource-intensive, which limit access for most students. To explore a cost-effective alternative to hands-on AEC learning, augmented reality (AR) is considered as a simulation of authentic learning in these domains. In this study, a full-scale building redesign and construction experience is presented, in which students virtually perform design analysis, planning, and construction of renovations to an existing structure in an AR environment. As students participate in the simulation, they reflect on their process and the realism of the experience. Student behaviors and perceptions are analyzed to determine where the experience showed evidence of authentic learning characteristics and where the experience differs from traditional authentic learning. Ultimately, the results indicate strong evidence that this AR-enabled design and construction experience promotes higher-level thinking, with mixed results on which components of the experience felt realistic or unrealistic. These findings suggest that, with informed and intentional use, AR has a high potential to simulate authentic learning and achieve high-value, evidence-based learning outcomes. Educators may find these results useful in adopting AR as a tool for simulating authentic learning in AEC or other fields in which true authentic environments are not readily available.
Time-Optimal Trajectory Planning for the Manipulator Based on Improved Non-Dominated Sorting Genetic Algorithm II

To address the issues of low efficiency and lengthy running time associated with trajectory planning for 6-degree-of-freedom manipulators, this paper introduces a novel solution that generates a time-optimal path for a manipulator while adhering to its kinematic limitations. The proposed method comprises several stages. Firstly, the kinematics of the manipulator are analyzed. Secondly, the manipulator’s joint-space path points are interpolated via the quintic B-spline curve. Subsequently, the non-dominated sorting genetic algorithm II (NSGA-II) is improved by applying reinforcement learning to optimize its crossover and mutation probabilities, and the variable neighborhood search (VNS) algorithm is integrated to enhance its local search capability. Finally, the joint increments and running time of the manipulator are optimized using the improved NSGA-II, and the time-optimal trajectory is then determined by simulation on MATLAB. Furthermore, compared with other conventional optimization methods, the proposed approach has reduced the total running time by 19.26%, effectively improving the working efficiency of the manipulator.
Data-Augmentation for Bangla-English Code-Mixed Sentiment Analysis: Enhancing Cross Linguistic Contextual Understanding

In today’s digital world, automated sentiment analysis from online reviews can contribute to a wide variety of decision-making processes. One example is examining typical perceptions of a product based on customer feedbacks to have a better understanding of consumer expectations, which can help enhance everything from customer service to product offerings. Online review comments, on the other hand, frequently mix different languages, use non-native scripts and do not adhere to strict grammar norms. For a low-resource language like Bangla, the lack of annotated code-mixed data makes automated sentiment analysis more challenging. To address this, we collect online reviews of different products and construct an annotated Bangla-English code mix (BE-CM) dataset (Dataset and other resources are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/fokhruli/CM-seti-anlysis</uri> ). On our sentiment corpus, we also compare several alternative models from the existing literature. We present a simple but effective data augmentation method that can be utilized with existing word embedding algorithms without the need for a parallel corpus to improve cross-lingual contextual understanding. Our experimental results suggest that training word embedding models (e.g., Word2vec, FastText) with our data augmentation strategy can help the model in capturing the cross-lingual relationship for code-mixed sentences, thereby improving the overall performance of existing classifiers in both supervised learning and zero-shot cross-lingual adaptability. With extensive experimentations, we found that XGBoost with Fasttext embedding trained on our proposed data augmentation method outperforms other alternative models in automated sentiment analysis on code-mixed Bangla-English dataset, with a weighted F1 score of 87%.
Which Target to Focus on: Class-Perception for Semantic Segmentation of Remote Sensing

Deep Learning-based (DL) methods have dominated the task of semantic segmentation of remote sensing images. However, the sizes of different objects vary widely, and there is a great deal of label-noise due to the inevitable shadows. Therefore, there is an urgent need for a method that can precisely handle complex ground data. In this paper, we propose an Inter-Class Enhanced Network (ICEN) for representing features of varying sizes. It comprises two branches: Sparse Representation Network (SPN) and Feature Extraction Network (FEN). Then, a Class-Perception Block is inserted between the two branches to instruct the SPN’s low-level semantic features to be merged into the deeper network. Such a block can reduce label-noise in remote sensing image segmentation. In addition, the proposed EIRI provides a more precise classification process for target edges containing many misclassified points without requiring excessive computational overhead. The experimental results of our proposed Class-Perception Network (C-PNet) achieve competitive performance on the Vaihingen, Potsdam, LoveDA, and UAVid datasets.
A Hybrid Convolutional Neural Network and Support Vector Machine-Based Credit Card Fraud Detection Model

Credit card fraud is a common occurrence in today’s society because the majority of us use credit cards as a form of payment more frequently. This is the outcome of developments in technology and an increase in online transactions, which have given rise to frauds that have caused significant financial losses. In order to detect fraud in credit card transactions, efficient and effective approaches are needed. In this study, we developed a hybrid CNN-SVM model for detecting fraud in credit card transactions. The effectiveness of our suggested hybrid CNN-SVM model for detecting fraud in credit card transactions was tested using real-world public credit card transaction data. The architecture of our hybrid CNN-SVM model was developed by replacing the final output layer of the CNN model with an SVM classifier. The first classifier is a fully connected layer with softmax that is trained using an end-to-end approach, whereas the second classifier is a support vector machine that is piled on top by deleting the final fully connected and softmax layer. According to experimental results, our hybrid CNN-SVM model produced classification performances with accuracy, precision, recall, F1-score, and AUC of 91.08%, 90.50%, 90.34%, 90.41, and 91.05%, respectively.