Reviewerly/exHarmony huggingface dataset:
Argumentative patterns based on pragmatic argumentation at China’s diplomatic press conferences

Within the theoretical framework of Pragma-Dialectics, this research investigates prototypical argumentative patterns based on pragmatic argumentation that manifest themselves in the spokespersons’ argumentative replies at the regular press conferences of China’s Ministry of Foreign Affairs. As is shown by the research results, constrained by the institutional point of the communicative activity type (justifying China’s attitude and actions regarding controversial issues) and its institutional preconditions, argumentative patterns based on pragmatic argumentation prototypically occur in three variants, viz. the ‘solvency type’, the ‘maintenance type’, and the ‘counter-plan type’; it can be observed that in complex pragmatic argumentation each of these three variants may also occur in combination with other variants.
DyGAT: Dynamic stroke classification of online handwritten documents and sketches

Online handwriting is widely used in human-machine interface, education, office automation, and so on. Stroke classification for online handwritten documents and sketches aims to divide strokes into several semantic categories and is a necessary step for document recognition and understanding. Previous methods are essentially static in that they have to wait for the user to finish the whole sketch before making prediction. However, in practice, the more user-friendly way is to make real-time prediction as the user is writing. In this paper, we introduce Dynamic Graph ATtention network (DyGAT) to solve the dynamic stroke classification problem. The core of our method is to formalize a document/sketch into a multi-feature graph, in which nodes represent strokes, edges represent the relationships between strokes, and multiple nodes are applied to one stroke to control the information flow. The proposed method is general and is applicable to online handwritten data of many types. We conduct experiments on popular public datasets to perform sketch semantic segmentation, document layout analysis and diagram recognition, and experimental results show competitive performance. Particularly, the proposed method achieves stroke classification accuracies which are only slightly lower than those of static classification.
Digital semicovering and digital quasicovering maps

In this paper we introduce notions of digital semicovering and digital quasicovering maps. We show that these are generalizations of digital covering maps and investigate their relations. We will also clarify the relationship between these generalizations and digital path lifting.
Argument by Association: On the Transmissibility of Commitment in Public Political Arguments

Abstract In this paper, I examine the question of commitment transmissibility in public political arguments. I explore the idea that under certain conditions, arguers become accountable for the commitments of their “argumentative associates” (Mohammed 2019a). I present cases where arguers make discursive effort to distance themselves from an undesirable associate in order to avoid acquiring the associate’s commitments, as well as cases where arguers fail to do so and face the consequences. I discuss the concept of commitment in argumentation (e.g. Hamblin 1970, Walton &amp; Krabbe 1995), and I build on the scheme of guilt and honour by association (Groarke and Tindale 2004). The result is a nuanced normative view of arguments by association: the transmissibility of commitments between argumentative associates is a way to hold arguers accountable for the argumentative potential (Mohammed 2019b; see also Kjeldsen 2017; Serafis 2022) of their discourse, and yet, commitment transmissibility remains defeasible to avoid the over-attribution of commitment by association.
Emerging Technologies and Their Link to Digital Competence in Teaching

The new educational reality requires teachers to have a series of skills and competences that allow them to improve the teaching–learning process and therefore the quality of teaching, integrating technology and emerging technologies. In order to assess the competence level of teachers, a descriptive study was designed, in which 101 teachers from different stages and geographical locations in Spain took part and were administered the DigCompEdu Check-in questionnaire. The results show average levels of teachers’ digital competence (B1 and B2, particularly), and an incipient use of emerging technologies by teachers, with less than 50% of the sample not using these technologies in their daily classroom activities, although those who show a higher level of digital competence are also those who integrate them more in their daily work. The results correspond with similar studies, corroborating the average level of teachers’ digital skills.
FLEXIBLE ADAPTIVE MARINE PREDATOR ALGORITHM FOR HIGH-DIMENSION OPTIMIZATION AND APPLICATION IN WIND TURBINE FAULT DIAGNOSIS

The marine predator algorithm (MPA) is the latest metaheuristic algorithm proposed in 2020, which has an outstanding merit-seeking capability, but still has the disadvantage of slow convergence and is prone to a local optimum. To tackle the above problems, this paper proposed the flexible adaptive MPA. Based on the MPA, a flexible adaptive model is proposed and applied to each of the three stages of population iteration. By introducing nine benchmark test functions and changing their dimensions, the experimental results show that the flexible adaptive MPA has faster convergence speed, more accurate convergence ability, and excellent robustness. Finally, the flexible adaptive MPA is applied to feature selection experiments. The experimental results of 10 commonly used UCI high-dimensional datasets and three wind turbine (WT) fault datasets show that the flexible adaptive MPA can effectively extract the key features of high-dimensional datasets, reduce the data dimensionality, and improve the effectiveness of the machine algorithm for WT fault diagnosis (FD).
A Novel Coupled Code Shifted M-ary Differential Chaos Shift Keying Modulation

In this paper, a novel coupled code shifted M-ary differential chaos shift keying modulation scheme is proposed as a simple and low-cost solution to reference removal. In this scheme, only two information-bearing signals are transmitted. Both signals are modulated by selected Walsh code sequences with specific indices determined by the index bits, and one of them also carries an additional M-ary information symbol. Since these two information-bearing signals are coupled by the same chaotic message bearer, data bits can be easily recovered according to the correlation between these two signals so that the transmission of reference signals can be completely avoided. Theoretical Bit Error Rate (BER) expressions are derived over the additive white Gaussian noise and multipath Rayleigh fading channels. Furthermore, simulations and comparisons indicate that the proposed system can achieve better BER performance with lower complexity.
Probabilistic prediction with locally weighted jackknife predictive system

Abstract Probabilistic predictions for regression problems are more popular than point predictions and interval predictions, since they contain more information for test labels. Conformal predictive system is a recently proposed non-parametric method to do reliable probabilistic predictions, which is computationally inefficient due to its learning process. To build faster conformal predictive system and make full use of training data, this paper proposes the predictive system based on locally weighted jackknife prediction approach. The theoretical property of our proposed method is proved with some regularity assumptions in the asymptotic setting, which extends our earlier theoretical researches from interval predictions to probabilistic predictions. In the experimental section, our method is implemented based on our theoretical analysis and its comparison with other predictive systems is conducted using 20 public data sets. The continuous ranked probability scores of the predictive distributions and the performance of the derived prediction intervals are compared. The better performance of our proposed method is confirmed with Wilcoxon tests. The experimental results demonstrate that the predictive system we proposed is not only empirically valid, but also provides more information than the other comparison predictive systems.
FTN–VQA: MULTIMODAL REASONING BY LEVERAGING A FULLY TRANSFORMER-BASED NETWORK FOR VISUAL QUESTION ANSWERING

Visual Question Answering (VQA) is a multimodal task, which requires understanding the information in the natural language questions and paying attention to the useful information in the images. So far, the solution of VQA tasks can be divided into grid-based methods and bottom-up-based methods. The grid-based method directly extracts the semantic features of the image by leveraging the convolution neural network (CNN), so it has a praiseworthy computational efficiency, but the global convolution feature ignores the information of the key area and causes the performance bottleneck. The bottom-up-based method needs to detect potentially problem-related objects by using some object detection frameworks, e.g. Faster RCNN, so it has better performance, but the computational efficiency is reduced due to the computation of Region Proposal Network (RPN) and Non-Maximum Suppression (NMS). Based on the aforementioned reasons, we propose a fully transformer-based network (FTN) that can maintain a balance between computational efficiency and accuracy, which can be trained end-to-end and consists of three components: question module, image module, and fusion module. Meanwhile, the image module and the question module are visualized to explore the operating rules of the transformer. The experiment results demonstrate that the FTN can focus on key information and objects in the question module and the image module, and our single model has reached 69.01% accuracy on the VQA2.0 dataset, which is superior to the grid-based methods. Although FTN fails to surpass a few state-of-the-art bottom-up-based methods, the FTN has obvious advantages in computational efficiency. The code will be released at https://github.com/weixiang-xu/FTN-VQA.git.
SUPER-LARGE-SCALE DATA ANALYSIS FOR ELECTRONIC HEALTH RECORD WITH ECML

With the deepening of hospital informatization construction, the electronic health record (EHR) system has been widely used in the clinical diagnosis and treatment process, resulting in a large amount of medical data. Electronic medical records contain a large amount of rich medical information, which is an important resource for disease prediction, personalized information recommendation, and drug mining. However, the medical information contained in electronic medical records cannot be automatically acquired, analyzed and utilized by computers. In this paper, we utilize machine learning algorithms for intelligent analysis of large-scale electronic medical records to explore and develop general methods and tools suitable for electronic medical record analysis in medical databases. This is of great value for summarizing the therapeutic effects of various diagnosis and treatment programs, disease diagnosis, treatment, and medical research. We propose an ECML-based intelligent analysis method for electronic medical records. First, we perform data preprocessing on the electronic medical record. Second, we design an intelligent analysis method for electronic medical records based on a deep learning model. Third, we design a model hyperparameter optimization method based on evolutionary algorithms. Finally, we compare and analyze the performance of the proposed model through experiments, and the experimental results show that the model proposed in this paper has good performance.
Maneuver-based trajectory planning with fast collision check for autonomous vehicles in a low-speed road environment

For autonomous vehicles driving in a low-speed road environment like complex campus, there are several peculiarities such as two-way lanes, break-in obstacles, smaller safety margin, etc. In this paper, a novel maneuver-based trajectory planning scheme is designed for autonomous vehicles driving in a low-speed environment with uncertain dynamic obstacles. First, a quintic polynomial is adopted to generate candidate trajectories and we develop a fast collision check algorithm to guarantee safety. In contrast to the existing methods, our collision check can reduce computation time with less conservatism. To balance the conflict between overtaking and lane-keeping, varying maneuvers are proposed to determine different weights in cost function for selecting the optimal trajectory. Simulations with uncertain low-speed obstacles are conducted to evaluate the performance of our proposed method.
Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques

Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed.
YouTube and Movie Recommendation System Using Machine Learning

YouTube, Netflix and some other OTT applications follow a drastic and systematic approach to how they portray movie recommendations to their users in order to keep them attracted and interested in their websites and applications. The concepts of Machine Learning and Artificial Intelligence are being used in so many different ways ranging from smallest keyword suggestions while users’ type, to the highly complex recommendation systems such as YouTube, that we solely use so often. In other words, recommendation system is used to suggest similar items to the user. The items can be movies, products, videos or anything depending on organizations or industries. Some modes of approach includes either using a candidate generation model or a ranking model to sort various forms of information which could vary from time spent while watching the videos to the age group of people watching the same videos. proposed work implemented using KNN machine learning model and obtained an accuracy of 98%.
Hand Gesture based driver-vehicle interface Framework for automotive In-Vehicle Infotainment system

The automotive in-vehicle infotainment system is an integral part of the four wheels these days. Driver vehicle interface becomes critical in the vehicle infotainment system. Therefore, it is necessary to develop an intelligent framework to reduce human and physical vehicle interaction. Hand gestures were used as an input modality to reduce visual and cognitive demands. This study has proposed a Single Shot multi-box Detector (SSD) based hand gesture vehicle interface framework to interact with a driver without any eye contact. The SSD model is used to detect hand gestures. Convolution Neural Network (CNN) is used to classify the gesture and make the right decision to control the in-vehicle infotainment system. The five independent hand gesture-based vehicle infotainment control is considered to reduce physical interaction and improve driver safety. To test and validate the proposed framework, a virtual experiment testbed is created using the Unreal Engine. The outcomes demonstrate the efficacy of the proposed work.
One Secret for a High Citation Rate

The relevance of the technical results included in a scientific paper is proved by the quantity of citations received by the article over the years [...]
Identification and Classification of Breast Cancer using Multilayer Perceptron Techniques for Histopathological Image

Among the many types of cancer that affect women, breast cancer (BC) is one of the most well-known. By analyzing and predicting BC, the condition can be effectively treated by preventing future medical issues. Machine learning (ML) is often regarded as the suitable approach for BC detection due to its effectiveness in analyzing complex BC datasets. Researchers rely on classification to make sense of the vast amounts of medical data they collect in their quest to identify breast cancer. To make the distinction between benign and malignant breast cancers without resorting to invasive surgery, a precise and consistent diagnostic approach is required for early detection. The model is trained using cancer data obtained from the Kaggle database. Multilayer Perceptron is able to classify data with an accuracy of 85%. Our anticipated algorithm’s primary function is illness categorization and diagnosis. When used in conjunction with other cancer detection methods, MLP improves the likelihood of a cancer diagnosis being made in time for the patient to receive treatment when it is most effective.
Sports Applications of Biomechanics Wearable Sensors using IoT

The study on how athletes move during athletic competition is known as sport biomechanics. Its goals are to evaluate athlete performance scientifically, provide coaches with useful tools and training suggestions for athletes, and reduce the likelihood of player injury. Small sensors worn by the user can track their movements and collect data while they play sports. The goal of this paper is to create low-cost, wearable bionic sensors capable of monitoring functional motions, workload, biomechanical, and bio vital data. This improves athletic performance while reducing injuries. Wearable sensors should provide continuous physiological data to players, which can then be used to develop nonrestrictive training routines for each player. Sensors should be required for athletes.
Self-Adaptive Multimedia Networked System for Effective Real-Time Feedback

In the last few years, surveillance cameras have gained much popularity mainly due to their convenience, flexibility, and portability. As crimes are increasing, so there is a need to enhance the existing video surveillance system. There are several flaws and Challenges in the existing system regarding malfunctioning of systems, system failure, backup procedures, and self-adaptation mechanisms, and so on. This study has developed an algorithm to make the existing system intelligent to take its own decision i.e., to execute the backup procedure, generate a warning message, and execute a self-orientation mechanism in case of any emergency.
Distance based Similarity Search with Distinct Encrypted Image Storage on Cloud

Secure picture recovery just on cloud has become increasingly popular in recent years as a result of the introduction of cloud computing. Both businesses and people frequently use the internet to manage and store their sensitive information, including such old photos and personal health records, due to its high level of convenience and financial savings. The typical method would be to encrypt the information prior to it being exported to using cloud to guarantee the secrecy of the information. The retrieval of information from encrypted data, for instance, may be impossible due to conventional encryption. The difficult issue in the cipher - text scenario is how to accomplish an effective retrieval while maintaining client anonymity. This study suggests a cutting-edge image retrieval method that achieves excellent efficiency and confidentiality over encrypted cloud data. An indexing tree is frequently used in the information extraction strategy to increase search performance. The security of private cloud data, including outsourced photos, the search tree, and query requests, is also a major concern. First, use an algorithm for extracting features from integrated picture features, which are made up of fundamental aspects like colour and shape. In particular, proposed technique can reach logarithmic search time thanks to the use of a balancing index tree. Second, the picture and query feature are encrypted using the secure inner product. Additionally, put in place a method to detect duplicate image material. Finally, extract the data owner details based on uploaded user-submitted image attributes that have been linked with distance measurements.
FPGA –based Optimized Design of Montgomery Modular Multiplier using Karatsuba Algorithm

Better area and delay performance is the goal of the modulo multiplier architecture that has been created in this paper. The design's architecture increases both the maximum frequency and the area that is occupied on the intended FPGA. Lightweight elliptic curve encryption is constructed using the suggested architecture as a modular multiplier over general G(p). In this design, radix-2 Montgomery Modular Multiplication structure is enhanced. In order to decrease the critical route latency and raise the maximum frequency, the suggested architecture does not perform subtraction or multiplication operations, instead it performs only one pre-calculated addition operation, which reduces the loops of process in computing. When compared to other state-of-the-art modular multipliers, these multipliers' accuracy is equivalent with better area and delay.