Reviewerly/exHarmony huggingface dataset:
Mask Constraint Based Image Local Style Transfer Algorithm

Aiming at the problem that the style transfer of the specified area of the image can not be carried out directly in practical application, an image local style transfer algorithm based on mask constraint is proposed.With the help of deep lab V3+ algorithm, the content image is segmented and its target area is extracted,a pixel by pixel optimized image style migration algorithm based on Gram matrix is used for image local style migration,redefine the content and style loss function of image local style migration,on the one hand, the mask matrix generated in semantic segmentation is used to standardize the range of parameters to be learned,on the other hand, the mask matrix is used to constrain the region calculated by the partial style loss function to remove the redundant region.Experimental results show that the improved algorithm has better local style conversion ability and speeds up the convergence speed of the algorithm.
Clustering of Cervical Histopathology Images Based on Minimum Spanning Tree

This paper proposes a graph based unsupervised learning (GBUL) clustering approach that uses features of graphs to identify different tissue structures in images, aiding histopathologists to quickly identify lesion areas and improve the accuracy of diagnosis. The first-stage rough clustering was performed by applying color features and k-means based on graph theory in Hematoxylin-eosin (H&E) stained cervical histopathology images. By applying a skeletonization based node generation (SBNG) approach, the generated nodes are approximated as a distribution of cervical cell nuclei, a minimum spanning tree (MST) is constructed from the generated nodes and their geometric features are extracted, followed by clustering by applying the k-means algorithm again. The important topological information hidden in histopathology images is applied to solve the cervical histopathology image clustering (CHIC) problem, and the function of mouse manual annotation to assist histopathologists is provided. CHIC based on MST can more accurately distinguish the tissue structure of high, medium and low density adherent cells. Combined with suspicious lesion points added manually by the physician, it can semi-automatically predict the cancer risk of tissues, rapidly identify lesion areas and improve the accuracy and efficiency of diagnosis.
All You Need is Sex for Diversity

Maintaining genetic diversity as a means to avoid premature convergence is critical in Genetic Programming. Several approaches have been proposed to achieve this, with some focusing on the mating phase from coupling dissimilar solutions to some form of self-adaptive selection mechanism. In nature, genetic diversity can be the consequence of many different factors, but when considering reproduction Sexual Selection can have an impact on promoting variety within a species. Specifically, Mate Choice often results in different selective pressures between sexes, which in turn may trigger evolutionary differences among them. Although some mechanisms of Sexual Selection have been applied to Genetic Programming in the past, the literature is scarce when it comes to mate choice. Recently, a way of modelling mating preferences by ideal mate representations was proposed, achieving good results when compared to a standard approach. These mating preferences evolve freely in a self-adaptive fashion, creating an evolutionary driving force of its own alongside fitness pressure. The inner mechanisms of this approach operate from personal choice, as each individual has its own representation of a perfect mate which affects the mate to be selected. In this paper, we compare this method against a random mate choice to assess whether there are advantages in evolving personal preferences. We conducted experiments using three symbolic regression problems and different mutation rates. The results show that self-adaptive mating preferences are able to create a more diverse set of solutions when compared to the traditional approach and a random mate approach (with statistically significant differences) and have a higher success rate in three of the six instances tested.
GPAM: Genetic Programming with Associative Memory

We focus on the evolutionary design of programs capable of capturing more randomness and outliers in the input data set than the standard genetic programming (GP)-based methods typically allow. We propose Genetic Programming with Associative Memory (GPAM) – a GP-based system for symbolic regression which can utilize a small associative memory to store various data points to better approximate the original data set. The method is evaluated on five standard benchmarks in which a certain number of data points is replaced by randomly generated values. In another case study, GPAM is used as an on-chip generator capable of approximating the weights for a convolutional neural network (CNN) to reduce the access to an external weight memory. Using Cartesian genetic programming (CGP), we evolved expression-memory pairs that can generate weights of a single CNN layer. If the associative memory contains 10% of the original weights, the weight generator evolved for a convolutional layer can approximate the original weights such that the CNN utilizing the generated weights shows less than a 1% drop in the classification accuracy on the MNIST data set.
Intelligent Evaluation System Using NLP

This article presents a comparative study of different evaluation systems. It does so with the proposal of a new architecture for the development of an autonomous generator of evaluation exercises that represents two main functionalities: automatic instantaneous evaluation and automatic improvement of the proposed questions. Our proposed solution allows each author to build a model of exercises according to his own pedagogical choices. This model is framed by the domain knowledge that each author must have, which will then be automatically instantiated for the generation of several exercise variants. The main feature of our generator is that it proposes different types of exercises. Each exercise is independent in its domain and adopted to the profile of each learner. This gives the possibility to use it in many disciplines. In addition, the use of domain knowledge reduces the workload of the author in defining the exercise templates and the associated corrections. Moreover, the automatic improvement of the proposed questions is done through the application of sentiment analysis techniques on the recorded answers.
AraBERT with GANs for High Performance Fine-Grained Dialect Classification

Arabic nations share the same mother tongue, Arabic. However the used vernacular language is different, that, in turn, may vary from one region to another. In this paper, we aim to identify various dialects by performing text classification. We distinguish between Moroccan, Algerian, Tunisian, Egyptian, and Lebanese Arabic dialects denoted respectively MDA, ADA, TDA, EDA and LDA. Aside from explaining the collecting process of system resources, we identify linguistic specificities that characterize each dialect. We build models from the preprocessed text using a combination of the pretrained model, AraBERT; and Generative Adversarial Networks (GANS). The work establishes the foundation of a dialect identification system by gathering freely available corpora and reaching the state-of-the-art.
A GAN-Based Superresolution Network for Infrared Power Equipment Images

Infrared image super-resolution algorithm is very important for intelligent inspection of power grid. Existing available super-resolution algorithms require that the infrared-visible image pairs are perfectly aligned, and only consider the two images numerically, ignoring the modallty differences between the infrared and visible images. Aiming on modallty discrepancy, generative-adversarial-based (GAN) network are designed to combine the information of generated corresponding visible image with the original infrared image; for the input matching limitation, the texture details of the reference image are used to adaptively generate high-resolution image. Experiments on infrared images of power devices data demonstrate that effectiveness and robustness of our method when applying to super-resolution task.
Hierarchical Attention Networks for Multispectral Image Compression

Aiming at the different i mportance a nd p ixel s ize o f feature information at different p ositions o f m ultispectral i mages, t his paper proposes an improved ConvNext architecture for multispectral image compression based on swin transformer, and designs the corresponding decoding network while adding multi-scale channel attention. The multispectral image obtains hierarchically mapped feature information through forward coding network, and then obtains the binary code stream through quantization and entropy coding. The reverse decoding network recovers the original image through hierarchical structure and upsampling, and the rate distortion optimization balances the reconstructed image quality and bit rate in end-to-end overall architecture. The experimental results show that at the same bit rate, the PSNR of the proposed method is better than that of the existing JPEG2000.
C-GZS: Controllable Person Image Synthesis Based on Group-Supervised Zero-Shot Learning

The objective of person image synthesis is to generate an image of a person that is perceptually indistinguishable from an actual one. However, the technical challenges that occur in pose transfer, background swapping, and so forth ordinarily lead to an uncontrollable and unpredictable result. This paper proposes a zero-shot synthesis method based on group-supervised learning. The underlying model is a twofold auto-encoder, which first decomposes the latent feature of a target image into a disentangled representation of swappable components and then extracts and recombines the factors therein to synthesize a new person image. Finally, we demonstrate the superiority of our work through both qualitative and quantitative experiments.
Link-Rot in Web-Sourced Multimedia Datasets

The Web is increasingly used as a source for content of datasets of various types, especially multimedia content. These datasets are then often distributed as a collection of URLs, pointing to the original sources of the elements. As these sources go offline over time, the datasets experience decay in the form of link-rot. In this paper, we analyze 24 Web-sourced datasets with a combined total of over 270 million URLs and find that over 20% of the content is no longer available. We discuss the adverse effects of this decay on the reproducibility of work based on such data and make some recommendations on how they could be mediated in the future.
Multispectral Image Compression Based on Improved Residual Network

In view of the fact that multispectral image has extensive application prospects and contains rich information, how to extract features efficiently is crucial for multispectral image compression. This paper proposed a new compressed framework which utilizes ConvGRU and contextual transformer respectively as the backbone of the spectral attention and Cot block to capture the spectral and spatial features. Additionally, residual units are used to explore deeper features to reserve fine details of image. The image data is firstly fed into the encoder which is composed of aforementioned units and then the quantizer and entropy encoder to convert data into the bit stream. The rate distortion optimization is utilized to perfect the reconstructed image better. Our method is compared with JPEG2000 via PSNR. The experimental results demonstrate that the method outperforms JPEG2000.
Fuzzy Ensemble Clustering Based on Self-Coassociation and Prototype Propagation

Fuzzy clustering ensemble that combines multiple fuzzy clustering results can obtain more robust, novel, stable, and consistent clustering result. The research about fuzzy clustering ensemble is still in the initial stage. Due to the special information expression, excellent clustering ideas are not well-practiced in fuzzy clustering ensemble and the performance of fuzzy clustering ensemble still has a large improvement space. In data clustering, prototype-based clustering is effective and efficient. The main idea of prototype-based clustering is discovering prototype samples to represent clusters and assigning samples to the represented clusters. In this article, we draw the idea of prototype-based clustering to fuzzy clustering ensemble and handle the problems of how to discover prototype samples based on a set of fuzzy clustering results and how to assign the samples without accessing the original data features. First, we propose a self-coassociation measure of a sample and discover its natural ability to evaluate the sample's local density. The rationality of the prototype samples discovered based on self-coassociation is theoretically analyzed and visually shown on eight artificial data sets. Then, we propose a prototype propagation method to assign data samples gradually. The working mechanism of the proposed sample assignment method is visually shown in the image segmentation scene. Finally, we develop a fuzzy clustering ensemble method based on self-coassociation and prototype propagation. The effectiveness of the proposed method is illustrated by comparing it with eight representative methods on benchmark datasets.
Genetic-Novelty Oversampling Technique for Imbalanced Data

Imbalance data is in important topic vexed researchers in practice of classification problems. A data is imbalanced if the distributions of categories are not approximately equally represented. The class with small samples is called minority class, while the other classes form the majority class. Standard learning classifiers tend to misclassify the minority samples; they assume that the distribution of data is relatively balanced. However in real world application, the corrected prediction of minority samples is more valuable than correctly classify samples belonging to the majority class. In this paper, we propose GNOT a novel oversampling strategy that combines algorithm genetic concept and novelty detection technique to generate consistent with the original distribution of the minority class while avoiding outliers. We tested GNOT on seven real-world imbalanced datasets. Our experimental analysis shows that GNOT can effectively improve the performance of classifiers in terms of G-mean and F1-measure.
DATM: A Novel Data Agnostic Topic Modeling Technique With Improved Effectiveness for Both Short and Long Text

Topic modelling is important for tackling several data mining tasks in information retrieval. While seminal topic modelling techniques such as Latent Dirichlet Allocation (LDA) have been proposed, the ubiquity of social media and the brevity of its texts pose unique challenges for such traditional topic modelling techniques. Several extensions including <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">auxiliary aggregation</i> , <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">self aggregation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">direct learning</i> have been proposed to mitigate these challenges, however some still remain. These include a lack of consistency in the topics generated and the decline in model performance in applications involving disparate document lengths. There is a recent paradigm shift towards neural topic models, which are not suited for resource-constrained environments. This paper revisits LDA-style techniques, taking a theoretical approach to analyse the relationship between word co-occurrence and topic models. Our analysis shows that by altering the word co-occurrences within the corpus, topic discovery can be enhanced. Thus we propose a novel data transformation approach dubbed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">DATM</i> to improve the topic discovery within a corpus. A rigorous empirical evaluation shows that DATM is not only powerful, but it can also be used in conjunction with existing benchmark techniques to significantly improve their effectiveness and their consistency by up to 2 fold.
Interacting Robots in an Artificial Evolutionary Ecosystem

In Evolutionary Robotics where both body and brain are malleable, it is common practice to evaluate individuals in isolated environments. With the objective of implementing a more naturally plausible system, we designed a single interactive ecosystem for robots to be evaluated in. In this ecosystem robots are physically present and can interact each other and we implemented decentralized rules for mate selection and reproduction. To study the effects of evaluating robots in an interactive ecosystem has on evolution, we compare the evolutionary process with a more traditional, oracle–based approach. In our analysis, we observe how the different approach has a substantial impact on the final behaviour and morphology of the robots, while maintaining decent fitness performance.
A Power Grid Accident Early Warning Method Considering Multi-factor Coupling

The power grid is an important infrastructure for the development of the national economy and society. It is of great significance to carry out research on the early warning method of power grid accidents to ensure the safe and stable operation of the power grid. In this paper, a power grid accident early warning method considering multi-factor coupling is proposed, based on conditional deep convolutional generative adversarial networks to generate minority class samples to achieve dataset balance. The depth features of the two-dimensional input samples were extracted by using a deep convolutional autoencoder network for the three types of disaster factors: meteorology, equipment and geographical environment. A deep self-encoding network is constructed, and the deep feature splicing of three types of disaster-causing factors is used as input, and a softmax classifier is added to the output layer of the network to construct a multi-factor coupled power grid accident early warning model to realize the prediction and early warning of power grid accidents. Numerical simulations verify the effectiveness and accuracy of the method proposed in this paper.
Inspection of Railway Track Facilities using Camcorder and Image Analysis

Appropriate maintenance of tracks is vital for the safe operation of railways. Proper managing track facilities is necessary to prevent buckling of rails. Due to social backgrounds of a shortage of workers, a decrease in skilled engineers, and a decrease in passenger income, inspection methods with a low-cost and no need of experience are desired. Therefore, we have developed a method that estimates each image's kilometers, rail gaps, wooden sleeper deterioration, and ballast shape using only inexpensive camcorders. This paper describes the outline of the method and its application results.
Efficient Autonomous Pathfinding Method for Virtual Environments

Pathfinding is a core component of most games today. Characters, animals and vehicles all move in a deliberate way. The NPCs must be able to avoid obstacles and identify the most efficient route from the starting point to the destination. A large number of games needs to solve similar multi-intelligent body movement. In this paper, we propose an improved flow field pathfinding algorithm that show its efficiency over other pathfinding methods during the experiments and allowing a large number of moving intelligences to reach the same destination in less time.
Credit Card Fraud Detection Using SVM, Decision Tree and Random Forest Supervised Machine Learning Algorithms

Since the inception of e-commerce payment systems, there have always been those who look for novel ways to get unauthorized access to another person’s assets. Payment cards are easy to use because they only require sending a short number of characters to the bank in order to identify your account and authorize the transaction. Due to their simplicity, they are more exposed. In this article, we’ll talk about how to detect credit card fraud. Our vision is to develop a fraud detection system that uses machine learning techniques like decision trees, random forest and Support Vector Machine (SVM).
Innovative Practices and the Contribution of Digital Technology to the University

This article questions the innovative pedagogical practices and the contribution of the digital technologies during the “pedagogical continuity” phase. It aims to clarify certain aspects of remote learning at the Ibn Tofail university. This non programmed learning method aims to find an effective response to the urgent situation related to the spread of the pandemic of “Covid-19”. To collect the necessary data, we used two types of online questionnaires. The first one was for professors in order to examine the way to manage remote courses (adaptation of courses - satisfaction - achievement of learning objectives - student participation). The second questionnaire was addressed to students, which was interesting not only to raise the satisfaction criteria and the constraints encountered but also to relieve their capacity to learn independently. Based on research work related to the role of digital technology in the act of learning, mainly remote learning, this study has made it possible to identify relevant information that would promote the improvement of courses via the Moodle platform. It highlighted the effectiveness of the digital resources used to ensure pedagogical continuity and in particular the ability of students to overcome the physical absence of the professor and to learn independently.
Design of Basic Physics Practicum Module Using Augmented Reality

The Basic Physics practicum is one of the compulsory courses that supply psychomotor abilities related to physical experiments.Basic physics practicum modules are developed to help students complete basic physics practicum courses well.This research aims to develop the design of basic physics practicum modules assisted by augmented reality technology to make it easier for students to perform experimental procedures.This research uses qualitative descriptive methods.The results of the analysis of student needs showed that almost 85% of students agreed and needed AR media in the Physics 2 practicum module to facilitate the implementation of the practicum.The results of the module analysis show that the module is designed by presenting a very comprehensive description of the material.Technology innovations are needed that help visualize practicum tutorial videos.The innovation design of practicum modules developed based on the results of the analysis using augmented reality in the presentation of experimental procedures.
Across the Cyberwaves: Twitter Campaigns for<i>Gaeilge</i>

Research increasingly shows how speakers and learners of minority languages exploit online spaces to forge communication networks and create and consume content in languages that are otherwise marginalized from central societal domains. While the connectivity afforded by internet technologies is imperative to computer-mediated communication, culturally authentic content and expressions of identity are central to meaning-making processes that may influence online participation (or not) in a particular language. We examine participation in two Twitter campaigns for the Irish language—both conceived to increase the visibility of Irish online but with drastically different outcomes. Using the Twitter interface to conduct a content analysis of tweets bearing the respective campaign hashtags, we reveal the discretionary approach to online engagement exhibited by minority-language users and explicate some of the social and communicative practices that contributed to the success of one campaign over the other, bolstered by a comprehensive campaign infrastructure and semantically enticing features.