Reviewerly/exHarmony huggingface dataset:
Automated Visual Story Synthesis with Character Trait Control

Visual storytelling is an art form that has been utilized for centuries to communicate stories, convey messages, and evoke emotions. The images and text must be used in harmony to create a compelling narrative experience. With the rise of text-to-image generation models such as Stable Diffusion, it is becoming more promising to investigate methods of automatically creating illustrations for stories. However, these diffusion models are usually developed to generate a single image, resulting in a lack of consistency be- tween figures and objects across different illustrations of the same story, which is especially important in stories with human characters.This work introduces a novel technique for creating consistent human figures in visual stories. This is achieved in two steps. The first step is to collect human portraits with various identifying characteristics, such as gender and age, that describe the character. The second step is to use this collection to train DreamBooth to generate a unique token ID for each type of character. These IDs can then be used to replace the names of the story characters in the image-generation process. By combining these two steps, we can create controlled human figures for various visual storytelling contexts.
A Survey of Semantic Web Based Recommender Systems for E-Learning

Recommender systems have been widely used in various domains such as e-commerce, e-learning and the entertainment sector especially for movie and music recommendation. E-learning has gained more attention recently, as the Covid-19 pandemic forced a mandatory shift from face-to-face education to online education. In this regard, taking into consideration the individual differences of learners such as learning styles, prior knowledge level, and motivation level play a vital role in acceptance and usage levels of e-learning systems by the learners and academic success levels of students using these e-learning systems. Semantic web technologies enable modeling these individual differences with ontologies and provide a valuable foundation for knowledge based recommender systems. Various learning resource or learning object ontologies that model learning materials and learner model ontologies that model learner profiles have been developed and used successfully in different educational recommender systems. These ontologies and interaction data of learners with e-learning systems provide valuable data for recommender systems to operate on. Collaborative filtering and content based filtering approaches have been used to analyze these data and implement educational recommender systems. Therefore, in this paper, we provide a review of semantic web based recommender systems conducted at the e-learning domain. We surveyed different aspects of educational recommender systems such as the data sources used for recommendations and the approaches utilized for implementation. Finally, we present the implications, limitations and future work directions in the field of educational recommender systems.
The Exploration of Instructional Content and Improvement on Music Media Elective Course

The purpose of this study is to provide a suggestion that implications for improving instructional content and expanding music media learning content by exploring music media courses. The method of this study is a literature review that analyzes the achievement standard statements of the Music and Media subject. Moreover it is comparing the Music and Media Art subjects in the curriculum with the results of the content analysis of the curriculum documents, drawing similarities and differences. In this regard, it is suggested that instructional perspective and improvement were achieved by exploring and comparing the overview, goals, and achievement standards of the Music and Media elective course presented in the 2022 revised National Korea Curriculum. This study is meaningful in suggesting instructional perspective and improvements for all students to learn digital music.
A Study on the Music Textbook Analysis and Teacher Perception in Elementary Music Career Education

This study suggests the direction of music and career education by investigating elementary school teachers' perceptions of music and career education based on the analysis of career education contents in the 2015 revised elementary school music textbooks for grades 5 and 6. To this end, 16 music textbooks were analyzed according to the four areas of the Ministry of Education's 2015 School Career Education Goals and Achievement Standards, and a survey was conducted on music and career education among 162 elementary school teachers. The results showed 212 cases of career education in the textbooks, with the highest proportion in the area of ‘understanding work and the world of work.’ Most teachers felt the need for music-related career education. They expected the development of career guidance programs using various media and experiential learning, as well as career learning materials in music textbooks. In addition, it is essential to increase students' interest and motivation in music, and it is necessary to guide music-related learning paths after elementary school.
Leveraging and Refining Image Recognition Technology for Intelligent Logistics Sorting Systems

Obstacles in the realm of target detection, tracking accuracy, real-time performance, and robustness have been identified within the context of intelligent logistics sorting systems, which incorporate image recognition technology. Furthermore, the critical role of detecting anomalous states in augmenting sorting efficiency and curtailing errors is underscored. Image recognition technologies of a conventional nature tend to suffer from limitations in their applicability and robustness. In practical working environments, a paucity of abnormal data from logistics sorting targets is observed, which inhibits the application of supervised learning methods of deep learning. Addressing these challenges, an unsupervised deep learning method is introduced for the detection of anomalous states in logistics sorting targets. This approach reinterprets the detection of logistics sorting targets as an anomaly detection problem and utilizes Variational AutoEncoders (VAE) for modeling the distribution of normal data. This method's dependency rests exclusively on normal data for training, thereby circumventing the need for a substantial quantity of abnormal samples. In practical deployments, the anomalous state of logistics sorting targets is discerned by the method through the computation of similarity and implementation of labeling algorithms, evidencing robustness, generalizability, and adaptability. Overall, this method is presented as an effective solution for the detection of anomalous states within intelligent logistics sorting scenarios, serving to decrease labeling costs, enhance detection accuracy and efficiency, and satisfying the practical requisites of logistics sorting systems for abnormal state detection.
Gated Recurrent Units and Recurrent Neural Network Based Multimodal Approach for Automatic Video Summarization

A typical video record aggregation system requires the concurrent performance of a large number of image processing tasks, including but not limited to image acquisition, pre-processing, segmentation, feature extraction, verification, and description. These tasks must be executed with utmost precision to ensure smooth system performance. Among these tasks, feature extraction and selection are the most critical. Feature extraction involves converting the large-scale image data into smaller mathematical vectors, and this process requires great skill. Various feature extraction models are available, including wavelet, cosine, Fourier, histogram-based, and edge-based models. The key objective of any feature extraction model is to represent the image data with minimal attributes and no loss of information. In this study, we propose a novel feature-variance model that detects differences in video features and generates feature-reduced video frames. These frames are then fed into a GRU-based RNN model, which classifies them as either keyframes or non-keyframes. Keyframes are then extracted to create a summarized video, while non-keyframes are reduced. Various key-frame extraction models are also discussed in this section, followed by a detailed analysis of the proposed summarization model and its results. Finally, we present some interesting observations about the proposed model and suggest ways to improve it.
Preferable academic work forms of medical students in the era of digitalization

The article presents the sociological study results on the preferred forms of medical students’ academic work in the era of digitalization. The study was carried out among students of the Volga Research Medical University using an online survey. The study purpose is to identify the attitude of various typological groups of medical students to various forms and methods of scientific and methodological support of the educational process, including the use of digital technologies. The work showed that behind the variety of medical students’ attitudes on the effectiveness of different forms of education, there is a willingness of young people to move away from traditional methods of obtaining professional knowledge and skills, and they clearly have a complex request for new forms of teaching, including using the Internet and electronic resources. The assessment of various forms and teaching methods has noticeable differences depending on the affiliation of students to various faculties. The article analyzes the students’ assessments of the various forms’ effectiveness of education using multidimensional data analysis, which made it possible to identify four types of student attitudes: 1) orientation to online learning; 2) live contact with the teacher; 3) electronic library; 4) work with summaries of educational materials. Each of these types contains its own set of preferred and rejected items
Visual Question Generation Answering (VQG-VQA) using Machine Learning Models

Presented automated visual question-answer system generates graphics-based question-answer pairs. The system consists of the Visual Query Generation (VQG) and Visual Question Answer (VQA) modules. VQG generates questions based on visual cues, and VQA provides matching answers to the VQG modules. VQG system generates questions using LSTM and VGG19 model, training parameters, and predicting words with the highest probability for output. VQA uses VGG-19 convolutional neural network for image encoding, embedding, and multilayer perceptron for high-quality responses. The proposed system reduces the need for human annotation and thus supports the traditional education sector by significantly reducing the human intervention required to generate text queries. The system can be used in interactive interfaces to help young children learn.
Web clustering Engines

Web clustering Engines are emerging trend in the field of information retrieval.They organize search results by topic, thus offering a complementary view to the flat ranked list returned by the conventional search engines.The search results returned by traditional search engines on different subtopics or meanings of a query will be mixed together in the list so that the user may have to sift through a large number of irrelevant items to locate those of interest.The Web clustering engines categorize the search results into different hierarchical groups/clusters and display those cluster labels.Hence the user can locate the desired document very fast.In this seminar we discuss different phases in the implementation of web clustering engines in detail and also incorporate some of the web clustering algorithms, their advantages and issues.We will familiarize some currently using web clustering engines.Some future research directions are also presented.Clustering is the act of grouping similar object into sets.The distance between the objects in the same cluster.
«Vocational training methods» as a key discipline in engineering pedagogy: problems and prospects for renewal

Introduction. The rapid changes taking place in the field of engineering, technology and production, affecting the needs of the economy and society as a whole, require a response in terms of updating the goals and content of training for the labor market from the vocational education system. Realizing that the training of specialized disciplines is based not only on the scientific and technical content of the disciplines taught, but also on the rules of didactics, the analysis of the experience of implementing one of the key academic disciplines in the framework of the engineering and pedagogical training of future specialists "Vocational Training Methods" requires close attention. The purpose of the article is to identify the degree of discrepancy in the content, implementation of the discipline, to diagnose the degree of student satisfaction with the quality of training, to determine the priority vectors for updating the content of the vocational training methodology. Materials and Methods. The theoretical and methodological basis of the study was the theory of the development of engineering and pedagogical education (M. G. Romantsev, S. J. Choi, etc.), content design (P. F. Kubrushko, O. V. Tarasyuk) and the principle of double lead (V. A. Fedorov, M. E. Oswald-Egg, U. Renold, etc.) in professional and pedagogical education. The method of analysis of curricula and work programs of the discipline "Vocational Training Methods" was applied using a two-stage sample. Also, in the period from October 21 to November 3, 2022, we conducted a survey among students of various universities (n=126) who had mastered the academic discipline. Results. Differences in the organization of teaching vocational training methods in different Russian universities were revealed in the following aspects: volume, content, forms, organizational and technical equipment, etc. labor intensity of independent work, etc.) aspects of satisfaction with the educational process of engineering and pedagogical training, priority sections of the academic discipline for more detailed mastering (designing new methods and pedagogical technologies, educational activities in vocational education, etc.). Discussion and Conclusions. It is proposed to organize the improvement of the vocational training methods in such key areas as the focus on expanding the range of innovative methods and pedagogical technologies and developing the future teachers’ creative potential.
Dampak Game Online Mobile Legends terhadap Siswa SD di Lingkungan XII Kecamatan Medan Denai Kelurahan Binjai

This article aims to discuss the impact of Mobile Legends on Elementary School students in Lingkungan XII, Medan Denai District, Binjai Village. This research is a type of quantitative research. The objects in this study were elementary school students in Lingkungan XII. The research was conducted on 55 students, including students in grades 5 and 6. The research was conducted using a simple random sampling system in sampling, and sampling using the Slovin formula with an error rate of 10%, a sample of 35 people was determined, and the data obtained was from a questionnaire. The theory used in this study is new media and the uses and effects theory. The results of the study show that online mobile legends games have an impact on elementary school students in Lingkungan XII Medan Denai, Binjai Village. Most of them started to be lazy to learn since they knew and played mobile legends online games.
MICDIR: Multi-scale inverse-consistent deformable image registration using UNetMSS with self-constructing graph latent

Image registration is the process of bringing different images into a common coordinate system — a technique widely used in various applications of computer vision, such as remote sensing, image retrieval, and, most commonly, medical imaging. Deep learning based techniques have been applied successfully to tackle various complex medical image processing problems, including medical image registration. Over the years, several image registration techniques have been proposed using deep learning. Deformable image registration techniques such as Voxelmorph have been successful in capturing finer changes and providing smoother deformations. However, Voxelmorph, as well as ICNet and FIRE, do not explicitly encode global dependencies (i.e. the overall anatomical view of the supplied image) and, therefore, cannot track large deformations. In order to tackle the aforementioned problems, this paper extends the Voxelmorph approach in three different ways. To improve the performance in case of small as well as large deformations, supervision of the model at different resolutions has been integrated using a multi-scale UNet. To support the network to learn and encode the minute structural co-relations of the given image-pairs, a self-constructing graph network (SCGNet) has been used as the latent of the multi-scale UNet — which can improve the learning process of the model and help the model to generalise better. And finally, to make the deformations inverse-consistent, cycle consistency loss has been employed. On the task of registration of brain MRIs, the proposed method achieved significant improvements over ANTs and VoxelMorph, obtaining a Dice score of 0.8013 ± 0.0243 for intramodal and 0.6211 ± 0.0309 for intermodal, while VoxelMorph achieved 0.7747 ± 0.0260 and 0.6071 ± 0.0510, respectively.
Memory-Efficient All-Pair Suffix-Prefix Overlaps on GPU

Obtaining overlaps between all pairs from billions of strings is a fundamental computational challenge in de novo whole genome assembly. This paper presents a memory-efficient, massively parallel algorithm that uses GPUs to find overlaps from large sequencing datasets with a very low probability of false positives. Here we use a Rabin-fingerprint-based indexing method which stores the strings with their fingerprints and uses them to generate the fingerprints of suffixes and prefixes. We then sort these fingerprints in hybrid CPU-GPU memory and stream them in GPU to find matches. Experiments show that our implementation can detect a trillion highly probable overlaps within 1.5 billion DNA fragments in just over two hours. Compared to the existing CPU-based approach, it routinely achieves speedups of 5–15x while having a collision rate of 1 in 20 million.
Map-based experience replay: a memory-efficient solution to catastrophic forgetting in reinforcement learning

Deep reinforcement learning (RL) agents often suffer from catastrophic forgetting, forgetting previously found solutions in parts of the input space when training new data. Replay memories are a common solution to the problem by decorrelating and shuffling old and new training samples. They naively store state transitions as they arrive, without regard for redundancy. We introduce a novel cognitive-inspired replay memory approach based on the Grow-When-Required (GWR) self-organizing network, which resembles a map-based mental model of the world. Our approach organizes stored transitions into a concise environment-model-like network of state nodes and transition edges, merging similar samples to reduce the memory size and increase pair-wise distance among samples, which increases the relevancy of each sample. Overall, our study shows that map-based experience replay allows for significant memory reduction with only small decreases in performance.
Hydraulic Engineering Construction Teaching Based on Case Method-Taking Wave Climbing as an Example

The course of hydraulic engineering construction teaching is rich in content, wide in scope of research, but short in duration.Teachers often do not have enough time to teach many knowledge points that need to be mastered in practical engineering.For example, the knowledge point of "wave climbing" is seldom reflected in the course, but practical engineering is inevitable.Based on the concept of case method, this paper designed the teaching path, incorporated relevant specifications, and taught the characteristics, sources, calculation processes and formulas of wave climbing in limited class hours, so that students can understand this knowledge point.
Confidence-Aware Training of Smoothed Classifiers for Certified Robustness

Any classifier can be "smoothed out" under Gaussian noise to build a new classifier that is provably robust to l2-adversarial perturbations, viz., by averaging its predictions over the noise via randomized smoothing. Under the smoothed classifiers, the fundamental trade-off between accuracy and (adversarial) robustness has been well evidenced in the literature: i.e., increasing the robustness of a classifier for an input can be at the expense of decreased accuracy for some other inputs. In this paper, we propose a simple training method leveraging this trade-off to obtain robust smoothed classifiers, in particular, through a sample-wise control of robustness over the training samples. We make this control feasible by using "accuracy under Gaussian noise" as an easy-to-compute proxy of adversarial robustness for an input. Specifically, we differentiate the training objective depending on this proxy to filter out samples that are unlikely to benefit from the worst-case (adversarial) objective. Our experiments show that the proposed method, despite its simplicity, consistently exhibits improved certified robustness upon state-of-the-art training methods. Somewhat surprisingly, we find these improvements persist even for other notions of robustness, e.g., to various types of common corruptions. Code is available at https://github.com/alinlab/smoothing-catrs.
Learning Adversarially Robust Sparse Networks via Weight Reparameterization

Although increasing model size can enhance the adversarial robustness of deep neural networks, in resource-constrained environments, there exist critical sparsity constraints. While the recent robust pruning technologies show promising direction to obtain adversarially robust sparse networks, they perform poorly with high sparsity. In this work, we bridge this performance gap by reparameterizing network parameters to simultaneously learn the sparse structure and the robustness. Specifically, we introduce Twin-Rep, which reparameterizes original weights into the product of two factors during training and performs pruning on the reparameterized weights to satisfy the target sparsity constraint. Twin-Rep implicitly adds the sparsity constraint without changing the robust training objective, thus can enhance robustness under high sparsity. We also introduce another variant of weight reparameterization for better channel pruning. When inferring, we restore the original weight structure to obtain compact and robust networks. Extensive experiments on diverse datasets demonstrate that our method achieves state-of-the-art results, outperforming the current sparse robust training method and robustness-aware pruning method. Our code is available at https://github.com/UCAS-LCH/Twin-Rep.
Correction: Li et al. Fringe Projection Profilometry Based on Saturated Fringe Restoration in High Dynamic Range Scenes. Sensors 2023, 23, 3133

The authors wish to make the following corrections to the original paper [...].