Reviewerly/exHarmony huggingface dataset:
New Strategies for Learning Knowledge Graph Embeddings: The Recommendation Case

Knowledge graph embedding models encode elements of a graph into a low-dimensional space that supports several downstream tasks. This work is concerned with the recommendation task, which we approach as a link prediction task on a single target relation performed in the embedding space. Training an embedding model requires negative sampling, which consists in corrupting the head or the tail of positive triples to generate negative ones. Although knowledge graph embedding models and negative sampling have extensively been investigated for link prediction, their combined use for performing recommendations over knowledge graphs remains largely unexplored in the literature. In this work, we propose two specialization strategies for training embedding models and performing knowledge graph-based recommendations. Both strategies first train an embedding model on the whole knowledge graph. Then, during a specialization phase, a dedicated negative sampling scheme is applied to refine the pre-trained model. Experimental results on two public datasets demonstrate that a simple strategy which refines a pre-trained model by sampling random negative tails for the target relation proves to be very effective. This strategy significantly improves performance with respect to traditional rank-based evaluation metrics as well as a newly introduced metric that reflects the semantic validity of the top-ranked candidate entities.
TRAINING CREATIVE THINKING FOR PRIMARY SCHOOL STUDENTS WITH THE APPROACH OF LEARNING THROUGH PLAYING

In the educational orientation of competence development of Vietnam's General Education Program 2018, creative thinking plays an important role, especially at the primary school level. Creative thinking is a key factor for students to develop problem-solving abilities in real life. The formation and development of creative thinking competence are to help students have the habit of independent thinking, creating new and unique ideas with high efficiency in problem-solving. The article approaches to analyze the characteristics of creative thinking in primary school students and the point of view of teaching according to the learning through playing approach, thereby determining the appropriate relationship when organizing teaching activities to improve the quality of creative thinking training for primary school students to meet the goals and requirements of the General Education Program 2018 of Vietnam.
Construction of a News Classification System Related to Information Security Incidents

In this study, based on previous studies on the impact of news related to information security incidents on stock prices, we attempted to extract news related to information security incidents by using sentence similarity calculation in BERT. The stock price fluctuations of the sample news and the extracted news were also measured by event study and compared to verify the effectiveness of the classification model. As a result, the news data with a similarity of about 85% approximates the results of event studies in previous studies. The potential of the classification system was demonstrated.
Architectural and mathematical fundamentals of improvement neural networks for classification of images

The article proposes ways to solve the problem of structural synthesis of neural networks architectures; based on non-traditional approaches to their analytical formalization and application of new ones data processing operations. The example of the CIFAR10 dataset proves the possibility of improving the accuracy of the solution image classification tasks within an alternative architecture with expandable input and narrowing structures of trained neural networks. The results of the application of input expanding image taken with Resizing and Conv2DTranspose layers at the input trained neural networks indicate the effectiveness of solving such classification problems by example CIFAR10 dataset. Achieved on the basis of the Xception neural network, the average classification accuracy of 10 classes CIFAR10 images are 97.3%. The side effect of pre-scaling images is leveling the accuracy of the classification of different classes, which allows us to consider such a change in size as an option data augmentation in the dataset. Tensor-matrix methods have been introduced for further development of this approach formalization of the description of neural networks on the basis of the penetrating end product of matrices and its block ones modifications. On this basis, a number of new convolution and maxpooling operations are proposed, as well as combinations on input of the narrowing segment not only symmetrically enlarged image, but also its variants obtained on the basis of a generalized penetrating product. It is about drawing images expanded by rows of pixels (horizontally) and columns (vertically), as well as a combination of several different options symmetrical image extensions on the principle of constructing a pyramidal segment of the PSPNet neural network.
Shakespeare in Cyborg Theatre: Immersive VR Theatre and the Cyborg-Subject

It is only recently that VR has become one of the most sought out technologies to create a narrative experience. However, as David Z. Saltz points out, ‘scholars and practitioners have yet to settle on a name to describe performances that incorporate digital media’. In this article, I extend Jennifer Parker-Starbuck’s concept of ‘cyborg theatre’ to the three technological retellings of Shakespeare – To Be With Hamlet, Hamlet 360, and The Under Presents: Tempest – and demonstrate immersive VR theatre as a site where the possibilities of shifting subjectivity materialize. The first section explores the nature of VR in theatre through its central function. Focusing on my experiences of the productions, I explore the cyborgean intersections in VR theatre via the sense of immersion, and highlight how the immersive power of VR incites reflections on the impact of new technologies on the body and the subject of the audience. Based on an examination of immersion, the second section focuses on the audience’s embodied subjectivity and argues immersive VR theatre as a site where alternative corporeal and technological configurations could be tested out. I am especially interested in how sensory immersion in VR, unlike existing claims on it, relates to the materiality as well as the imitative representation of a fictional world.
Research on Application of OpenGL-Based Game Design

In today’s social development, the level of information construction is getting higher and higher; the positive role of network construction innovation is becoming more and more significant; people’s daily life and work began to rely on the concept of network technology to achieve more needs. As the most common way of leisure and entertainment, games have been loved and valued by the social masses in the actual promotion, and people begin to use the game platform to release pressure and obtain happiness and achievement. Based on the analysis of the development trend of games in recent years, it can be seen that nowadays game design does not just stay in 2D mode for simple operation, and people pay more attention to the new experience brought by game content and vision. Based on the understanding of OpenGL game application editing interface (API), this paper deeply explores the development trend of 3D game market at home and abroad and carries out empirical analysis from the actual game design, so as to clarify the feasibility of game design based on OpenGL.
Compositional cubes: a new concept for multi-factorial compositions

Compositional data are commonly known as multivariate observations carrying relative information. Even though the case of vector or even two-factorial compositional data (compositional tables) is already well described in the literature, there is still a need for a comprehensive approach to the analysis of multi-factorial relative-valued data. Therefore, this contribution builds around the current knowledge about compositional data a general theoretical framework for k-factorial compositional data. As a main finding it turns out that, similar to the case of compositional tables, also the multi-factorial structures can be orthogonally decomposed into an independent and several interactive parts and, moreover, a coordinate representation allowing for their separate analysis by standard analytical methods can be constructed. For the sake of simplicity, these features are explained in detail for the case of three-factorial compositions (compositional cubes), followed by an outline covering the general case. The three-dimensional structure is analyzed in depth in two practical examples, dealing with systems of spatial and time dependent compositional cubes. The methodology is implemented in the R package robCompositions.
Temporal analysis of cooperative behaviour in a blockchain for humanitarian aid during the COVID-19 pandemic

In the last few years, there has been a growing interest in the subject of blockchain technology for good. Among the many endeavours, blockchain technology has lately been exploited to build complementary currencies in the sphere of humanitarian aid: currencies that support national economies to provide humanitarian aid and promote development. While there have been numerous research projects on complementary currencies (CCs) and their success, some critical aspects remain largely unexplored. First, even though cooperation is a key factor in the development of these systems, as local communities organize themselves in times of crisis, there is a lack of studies that investigate the cooperative behaviour in these systems and how it changes over time. Besides, there are only a few works studying these currencies during the recent crisis of the COVID-19 pandemic. In this work, we investigate Sarafu, a digital complementary currency based on blockchain technology. To support cooperation, Sarafu implements a special type of account, the group account, thus allowing the study of cooperation groups, that cannot be easily analyzed in other CC systems; furthermore, it was successfully used for humanitarian aid during the COVID-19 pandemic. We find that Sarafu users show strong cooperative behaviour, facilitated by the usage of these group accounts. Furthermore, we observe the increasing importance of cooperation groups over time, as well as differences over time in their spending behaviour. From the analysis, we highlight the presence of cooperation patterns and the importance of group accounts, a takeaway for current and future humanitarian projects.
Transferring Unconditional to Conditional GANs with Hyper-Modulation

GANs have matured in recent years and are able to generate high-resolution, realistic images. However, the computational resources and the data required for the training of high-quality GANs are enormous, and the study of transfer learning of these models is therefore an urgent topic. Many of the available high-quality pretrained GANs are unconditional (like StyleGAN). For many applications, however, conditional GANs are preferable, because they provide more control over the generation process, despite often suffering more training difficulties. Therefore, in this paper, we focus on transferring from high-quality pretrained unconditional GANs to conditional GANs. This requires architectural adaptation of the pretrained GAN to perform the conditioning. To this end, we propose hyper-modulated generative networks that allow for shared and complementary supervision. To prevent the additional weights of the hypernetwork to overfit, with subsequent mode collapse on small target domains, we introduce a self-initialization procedure that does not require any real data to initialize the hypernetwork parameters. To further improve the sample efficiency of the transfer, we apply contrastive learning in the discriminator, which effectively works on very limited batch sizes. In extensive experiments, we validate the efficiency of the hypernetworks, self-initialization and contrastive loss for knowledge transfer on standard benchmarks. Our code is available at https://github.com/hecoding/Hyper-Modulation.
Challenges and Benefits of Combining AI with Blockchain for Sustainable Environment

Since the emergence of Fourth Industrial Revolution, the concepts of Artificial Intelligence (AI) and blockchain had continued to receive growing applications. The technologies are prevalent in scientific domains and diverse research sectors such as agriculture, engineering, medical science, smart manufacturing and industries. The spread rate of the application of the technologies for innovation across all sectors had been phenomenal. On the other hand, quality of life has a direct link with continuous improvement in the quality of our environment – which can be achieved by deployment of advanced technology-based intelligent and control systems such as AI and blockchain. While AI and blockchain possess individual capabilities and potential contributions to environmental sustainability, the combining effects of the applications of both technologies to environmental sustainability domain could ensure robust deliverables and create numerous possibilities. This chapter therefore explores the benefits of the alliance of AI and blockchain in environmental sustainability as it relates to renewable energy and pollution prevention and presents some barriers to the efficacy of the synergy of the two technologies. Some solutions, where possible, are proposed to the challenges in order to harness the maximum benefit from alliance of the technologies as a positive force for sustainable environment.
Blood Cell Image Recognition Algorithm based on EfficientNet

Diagnosis of blood-related diseases usually requires identification of patient blood samples, and fast and accurate classification of blood cells can help improve the efficiency of doctors’ diagnosis. To address the problems of traditional manual methods that are time-consuming and error-prone, as well as the low accuracy of current automatic blood cell classification algorithms, this paper uses EfficientNet to classify blood cell images. We propose an EfficientNet-based blood cell image classification algorithm, using EfficientNet-B7 as the classification model and introducing CLAHE in data preprocessing to improve the image quality. The experiments were conducted on the BCCD dataset and compared with various classification networks and blood cell classification studies. The experimental results show that our proposed EfficientNet-based blood cell image classification algorithm achieves 99.6% accuracy on the BCCD dataset. The current optimal blood cell classification algorithm has only 94% accuracy on this dataset, and the algorithm in this paper outperforms it by 5.6 percentage points. This study will hopefully be used to replace the manual classification of blood cells by professional physicians.
Real-Time Traffic Counting on Resource Constrained Embedded Systems

Real-time traffic monitoring is crucial to meet the demand for intelligent traffic coordination. Adjusted green phases can improve traffic flow and lead to congestion avoidance. Therefore, in addition to object counting, it is crucial for optimal traffic monitoring to track object movement. In this paper, we propose a highly optimized framework for detecting, tracking and counting road users at intersections. The nano YOLOv5 is used for detection and a neural architecture searched network is proposed for feature extraction of the DeepSORT algorithm. Both are compressed using TensorRT. Between detected frames, an optical flow-based tracker provides consistent processing of tracked objects and relieves the embedded system of some computationally intensive neural network inference. Motion and box shape features are taken into account to improve robustness in re-identification and prevent swapping boxes.
Artificial intelligence computer-aided detection enhances synthesized mammograms: comparison with original digital mammograms alone and in combination with tomosynthesis images in an experimental setting

It remains unclear whether original full-field digital mammograms (DMs) can be replaced with synthesized mammograms in both screening and diagnostic settings. To compare reader performance of artificial intelligence computer-aided detection synthesized mammograms (AI CAD SMs) with that of DM alone or in combination with digital breast tomosynthesis (DBT) images in an experimental setting.We compared the performance of multireader (n = 4) and reading multicase (n = 388), in 84 cancers, 83 biopsy-proven benign lesions, and 221 normal or benign cases with negative results after 1-year follow-up. Each reading was independently interpreted with four reading modes: DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT. The accuracy of probability of malignancy (POM) and five-category ratings were evaluated using areas under the receiver operating characteristic curve (AUC) in the random-reader analysis.The mean AUC values based on POM for DM, AI CAD SM, DM + DBT, and AI CAD SM + DBT were 0.871, 0.902, 0.895, and 0.909, respectively. The mean AUC of AI CAD SM was significantly higher (P = 0.002) than that of DM. For calcification lesions, the sensitivity of SM and DM did not differ significantly (P = 0.204). The mean AUC for AI CAD SM + DBT was higher than that of DM + DBT (P = 0.082). ROC curves based on the five-category ratings showed similar proximity of the overall performance levels.AI CAD SM alone was superior to DM alone. Also, AI CAD SM + DBT was superior to DM + DBT but not statistically significant.
Two Case Studies on Implementing Best Practices for Software Process Improvement

Software Process Improvement requires significant effort related not only to the identification of relevant issues and providing an adequate response to them but also to the implementation and adoption of the changes. Best practices provide recommendations to software teams on how to address the identified objectives in practice, based on aggregated experience and knowledge. In the paper, we present the GÉANT experience and observations from the process of adopting the best practices and present the setting we have been using.
An Interactive Dashboard to Support Policymaking of Reopening School on Covid-19 Pandemic

In mid-April 2020, UNESCO monitored 191 countries and stated that around 1.723 trillion students in the world were affected by the policy of school from home. It is feared that school closures could hamper the provision of education services and could disrupt the education process which will affect the level of quality of education. There is still no creation of a computational model for the spread of covid as the main framework for schools reopening safely during the pandemic situation. Although there is already a framework from WHO and the government, there is no measuring tool that can evaluate the effect of reopening schools while the Covid-19 pandemic. For this reason, this research seeks to produce a model for the spread of Covid-19 as a basis for determining policies for safely reopening schools during the pandemic. In this research, we produced a recommendation to reopen face-to-face learning in the form of a dashboard. Recommendations are given by predicting the number of cases in each subdistrict using a predictive model. The prediction results are also combined with the factors that have been determined by the government to give recommendations. The allotment of recommendations process involves a critical factor analysis process where we identify which factors are dominant as a basis of a controllable pandemic.
Development of a Low-Cost Multipurpose Autonomous Navigation Robot

The pandemic has made us forcefully enter an era of contactless transactions. What can we do in such a situation? How can we help the people who are doing their best at the front to help us? This paper focuses on developing a robot that can serve anywhere and everywhere. The study describes the software and hardware development of an autonomous robot that can navigate through dynamic environmental surroundings. The proposed prototype can map the area with a Lidar and after which, when given goal locations in the generated map, can autonomously navigate to the desired position. It is also equipped with a wide-angle camera [Pi cam] to achieve real-time surveillance and implement person following and object recognition abilities. The designed robot can find suitable applications in UV sanitization, food and medicine delivery, and teleoperation purposes.
Classification Of Indian Sign Language Characters Utilizing Convolutional Neural Networks And Transfer Learning Models With Different Image Processing Techniques

In terms of visual-spatial modality, the sign language is considered to be a natural as well as a full-fledged language. It has all of the linguistic characteristics of spoken language (from phonology to syntax). Sign language is a form of communication in which the hands are used instead of words. It uses a variety of signs to convey thoughts and concepts. For ISL static character recognition, we propose a Convolutional Neural Network (CNN) architecture in this paper. Comparison of different feature extraction techniques tested on CNN architecture is done in this particular paper. We hand-crafted the dataset used to train the CNN model in order to come as near to the real-life scenario in which the model&#x2019;s viability would be assessed as possible. The proposed method was successfully implemented with a 99.90 percent accuracy, which is better than the majority of currently available methods.
Identification of Image Forgery based on various Corner Detection methods

Digital image indubitably has lost virginity in both its source and surroundings due to the encroachment of high-resolution camera, state-of-the-art image handling tools and contemporary personal computers. As a result, authenticating the integrity of digital image became very imperative and discovering any indications of falsification in the image has turned into a sizzling turf to carry out research for last few years. In this paper, image forgery has been identified using Min Eigen feature extraction based on Shi-Tomasi Corner Detection method which detects interest points. Initially, various corner detection and feature extraction methods have been studied and analysed to extract features from the input images. From the gray-scale image, distinctive localized features are extracted based on Harris feature extraction, surf features and fast features. Subsequently, Euclidean distance is calculated between the feature vectors of the images. Then the resultant feature values are further implemented using classifiers to obtain accurate result analysis.
A Comparative Study of Supervised and Unsupervised Machine Learning Algorithms on Consumer Reviews

For any organization involving consumers, reviews and feedbacks are quite important. For this purpose, the bulk of data is generated from various social networking sites in terms of reviews and feedbacks. In order to understand consumer&#x2019;s perception about an item, this research scrutinizes various supervised and unsupervised machine learning algorithms on two data sets. A comparative analysis is made for deliberating the efficiency of these algorithms on distinct datasets for text classification. This research is an attempt to find the best fit classifier for consumer&#x2019;s perception using sentiment analysis. So, in order to accomplish this objective, firstly text preprocessing techniques are applied on datasets then feature extraction techniques are applied on the processed data. Thereafter, classification and clustering are applied using supervised and unsupervised machine learning algorithms respectively. Further, these algorithms are evaluated and the result reveals that supervised machine learning algorithms especially Support Vector Machine (SVM) outperforms unsupervised machine learning algorithms for garments dataset. And Naive Bayes (NB), Logistic Regression (LR) outperforms unsupervised machine learning algorithms for restaurant dataset.