Reviewerly/exHarmony huggingface dataset:
Arbitrary style transfer with attentional networks via unbalanced optimal transport

Arbitrary style transfer aims to stylize the content image with the style image. The key problem of style transfer is how to balance the global content structure and the local style patterns. A promising method to solve this problem is the attentional style transfer method, where a learnable embedding of image features enables style patterns to be flexibly recombined with the content image, so local style patterns will be well preserved in the stylized image. However, current attentional style transfer methods cannot well preserve the global content structure. To solve this problem, a novel attentional style transfer network is proposed, that relies on Optimal Transport (OT) for computing the attention map. The proposed OT-based attention ensures the similarity between global distributions of the synthesized image and its corresponding style image. For the optimal transport computation, a regularized formulation is used, which not only allows an unbalanced optimal transport to address the deviational distributions but also improves the robustness of stylized results. The proposed method finds a well balance between the global content structure and local style patterns. Various experiments are conducted to demonstrate the superiority of the proposed method over state-of-the-art methods.
Immersive Inscribed Spaces – Bringing Virtuality to Written Artefacts for Humanities

Abstract Writing is an essential cultural technique, and the resulting artefacts are an important part of cultural heritage. The Cluster of Excellence ‘Understanding Written Artefacts’ is an interdisciplinary and cross-cultural long-term project dedicated to studying so-called ‘written artefacts (WA)’. Our work introduces immersive technologies such as virtual, augmented, and mixed reality to the research cluster for the first time. In this paper, we outline the scope of our research project and present our current implementations of immersive applications based on two scenarios involving inscribed spaces. So far, immersive technologies have not been used in academia to create research focused applications for exploring, analysing, and understanding WA within their inscribed space, including providing access to appropriate spatial and temporal contexts. Thus, we collaborate closely with researchers from the humanities to create interactive and immersive applications for the novel field of WA research. The results of our preliminary user study show high ratings in the sense of presence in the virtual environments and indicate that immersive spatial context could add new perspectives for understanding WA. We hope to provide valuable insights on the design of immersive applications to support future research in novel fields.
Searches among ATLA and Its Database Rivals: A Comparison Study of Search Results and Unique Results for Theological-Specific Research Questions

This original study formulates three hypothetical queries tailored to college-level theological research questions that focus on topics pertaining to biblical theology. The study runs searches for those queries in four religion-specific databases and two multidisciplinary databases in order to compare the total tally of peer-reviewed journal articles in each database along with the total unique results contained within each database. ATLA Religion Database with ATLA Serials performed best for both objectives. Meanwhile, Religion & Philosophy Collection (EBSCO) and Religion Database (ProQuest) proved helpful for some of the research queries. Consequently, Religion and Philosophy Collection (Gale) performed poorly for both objectives. The two multidisciplinary databases (Academic Search Complete and ProQuest Central) performed unevenly. ATLA represented the best choice if simply performing one search in a database; however, using all databases (other than Religion and Philosophy Collection, Gale), represented a stronger strategy for retrieving a high number of unique results, as the databases did not have a high number of shared resources.
Quantum entanglement with generalized uncertainty principle

We explore how the quantum entanglement is modified in the generalized uncertainty principle (GUP)-corrected quantum mechanics by introducing the coupled harmonic oscillator system. Constructing the ground state $\rho_0$ and its reduced substate $\rho_A = \mbox{Tr}_B \rho_0$, we compute two entanglement measures of $\rho_0$, i.e. ${\cal E}_{EoF} (\rho_0) = S_{von} (\rho_A)$ and ${\cal E}_{\gamma} (\rho_0) = S_{\gamma} (\rho_A)$, where $S_{von}$ and $S_{\gamma}$ are the von Neumann and R\'{e}nyi entropies, up to the first order of the GUP parameter $\alpha$. It is shown that ${\cal E}_{\gamma} (\rho_0)$ increases with increasing $\alpha$ when $\gamma = 2, 3, \cdots$. The remarkable fact is that ${\cal E}_{EoF} (\rho_0)$ does not have first-order of $\alpha$. Based on there results we conjecture that ${\cal E}_{\gamma} (\rho_0)$ increases or decreases with increasing $\alpha$ when $\gamma > 1$ or $\gamma < 1$ respectively for nonnegative real $\gamma$.
All by Myself: Learning individualized competitive behavior with a contrastive reinforcement learning optimization

In a competitive game scenario, a set of agents have to learn decisions that maximize their goals and minimize their adversaries' goals at the same time. Besides dealing with the increased dynamics of the scenarios due to the opponents' actions, they usually have to understand how to overcome the opponent's strategies. Most of the common solutions, usually based on continual learning or centralized multi-agent experiences, however, do not allow the development of personalized strategies to face individual opponents. In this paper, we propose a novel model composed of three neural layers that learn a representation of a competitive game, learn how to map the strategy of specific opponents, and how to disrupt them. The entire model is trained online, using a composed loss based on a contrastive optimization, to learn competitive and multiplayer games. We evaluate our model on a pokemon duel scenario and the four-player competitive Chef's Hat card game. Our experiments demonstrate that our model achieves better performance when playing against offline, online, and competitive-specific models, in particular when playing against the same opponent multiple times. We also present a discussion on the impact of our model, in particular on how well it deals with on specific strategy learning for each of the two scenarios.
Tumor deformation correction for an image guidance system in breast conserving surgery

Breast cancer is the most common cancer in women, and surgical resection is standard of care for the majority of breast cancer patients. Unfortunately, current reoperation rates are 10-29%. Uncertainty in lesion localization is one of the main factors contributing to these high reoperation rates. This work uses the linearized iterative boundary reconstruction approach to model patient breast deformation due to abduction of the ipsilateral arm. A preoperative supine magnetic resonance (MR) image was obtained with the patient's arms down near the torso. A mock intraoperative breast shape was measured from a supine MR image obtained with the patient's arm up near the head. Sparse data was subsampled from the full volumetric image to represent realistic intraoperative data collection: surface fiducial points, the intrafiducial skin surface, and the chest wall as measured with 7 tracked ultrasound images. The deformed preoperative armdown data was compared to the ground truth arm-up data. From rigid registration to model correction the tumor centroid distance improves from 7.3 mm to 3.3 mm, average surface fiducial error across 9 synthetic fiducials and the nipple improves from 7.4 ± 2.2 to 1.3 ± 0.7, and average subsurface error across 14 corresponding features improves from 6.2 ± 1.4 mm to 3.5 ± 1.1 mm. Using preoperative supine MR imaging and sparse data in the deformed position, this modeling framework can correct for breast shape changes between imaging and surgery to more accurately predict intraoperative position of the tumor as well as 10 surface fiducials and 14 subsurface features.
Detection of Camouflaged Drones using Computer Vision and Deep Learning Techniques

Nowadays the world is seeing rapid advancement in robotics and especially flying unmanned vehicles or drones. These drones are controlled manually or sometimes automated as unmanned scouts to scan entire areas. Unfortunately, because of the expanding fame of these drones, their capability to be abused has increased drastically, due to which security locked important locations are now vulnerable to either getting attacked or spied upon. An automated drone detection is not only a robust solution, but it is the most needed one. This paper highlights a drone detection method using computer vision to detect and track drones by creating a deep learning model. The YOLOv4 model is tested on the testing dataset, which consists of both usual and custom drone images. The model is also tested on unseen videos and performance of drone detection was tested and the FPS was recorded during the detection to recognize the speed of detection at various elevations levels of the drone. The technique utilized in this task is for certain changes in the YOLOv4 architecture which causes the model to perform better compared to what past related works evaluations offer and has been fruitful in scoring a mAP of 94.36&#x0025;. The normal FPS seen on various recordings for drone identification was near 36 on recordings with sky as background and 30.5 on recordings with dense background like trees and structures.
Creativity via a Genre Pedagogy to Promote EFL Indigenous Students’ Writing

This paper presents a conceptual framework that has been developed to promote creativity among English as a Foreign Language (EFL) Malaysian indigenous students, specifically in the teaching of writing. The objectives are to introduce the factors that contribute to indigenous students’ low English proficiency, highlight writing challenges of indigenous students, propose a genre pedagogical framework as a means to address their writing challenges and simultaneously, motivate their learning via creative practices, and discuss past studies on the efficacy of a genre-based writing pedagogy that promotes creativity. This paper employed an integrative review approach in sourcing and critically analysing relevant literature. The framework was developed to address the educational challenges of primarily providing learning opportunities to the marginalised indigenous minority in Malaysia, the Orang Asli, which concurrently bridges the English language gap between urban and rural schools in the country. By conceptualising a genre as a model for teaching EFL writing through the integrated theoretical lenses of socio-cultural learning as well as extensive secondary research, a framework for evaluating the potential of teaching genre-based EFL writing is derived, focussing on the creative aspect. Thus, this paper enhances understanding of the practicality of genre writing in addressing the writing challenges of low proficiency indigenous students. The proposed framework has the potential to serve as a reference point to other EFL teachers involved in teaching indigenous students globally. Keywords: education; education quality; empowerment; equal opportunities; genre-based approach
Enhancing the Accessibility of Knowledge Graph Question Answering Systems through Multilingualization

There are around 7000 languages spoken today in the world, yet English dominates in many research communities e.g., Knowledge Graph Question Answering (KGQA). The goal of a KGQA system is to provide natural language access to a knowledge graph. While many research works aim to achieve the best possible QA quality over English benchmarks, only a small share of them additionally focus on providing an equivalent experience for the ones who use such systems (i.e., accessibility). To fill this research gap, we investigate the multilingual aspect of the accessibility, which enables speakers of different languages (including low-resource and endangered languages) to interact with KGQA systems with an equivalent efficiency.
Calculation of Core-Excited and Core-Ionized States Using Variational Quantum Deflation Method and Applications to Photocatalyst Modeling

The possibility of performing quantum-chemical calculations using quantum computers has attracted much interest. Variational quantum deflation (VQD) is a quantum-classical hybrid algorithm for the calculation of excited states with noisy intermediate-scale quantum devices. Although the validity of this method has been demonstrated, there have been few practical applications, primarily because of the uncertain effect of calculation conditions on the results. In the present study, calculations of the core-excited and core-ionized states for common molecules based on the VQD method were examined using a classical computer, focusing on the effects of the weighting coefficients applied in the penalty terms of the cost function. Adopting a simplified procedure for estimating the weighting coefficients based on molecular orbital levels allowed these core-level states to be successfully calculated. The O 1s core-ionized state for a water molecule was calculated with various weighting coefficients, and the resulting ansatz states were systematically examined. The application of this technique to functional materials was demonstrated by calculating the core-level states for titanium dioxide (TiO2) and nitrogen-doped TiO2 models. The results demonstrate that VQD calculations employing an appropriate cost function can be applied to the analysis of functional materials in conjunction with an experimental approach.
Scaling R-GCN Training with Graph Summarization

Training of Relational Graph Convolutional Networks (R-GCN) is a memory intense task. The amount of gradient information that needs to be stored during training for real-world graphs is often too large for the amount of memory available on most GPUs. In this work, we experiment with the use of graph summarization techniques to compress the graph and hence reduce the amount of memory needed. After training the R-GCN on the graph summary, we transfer the weights back to the original graph and attempt to perform inference on it. We obtain reasonable results on the AIFB, MUTAG and AM datasets. Our experiments show that training on the graph summary can yield a comparable or higher accuracy to training on the original graphs. Furthermore, if we take the time to compute the summary out of the equation, we observe that the smaller graph representations obtained with graph summarization methods reduces the computational overhead. However, further experiments are needed to evaluate additional graph summary models and whether our findings also holds true for very large graphs.
University Student Status Management System Based on Blockchain

Student status management is a key part in university information construction and important basis for talents certification. Therefore, the authenticity and security of Student status information in the management system is particularly important. Blockchain is essentially a decentralized distributed account book, which has the technical characteristics of being difficult to tamper with and high in security, and can solve the problems of information openness and data security under the centralized framework. In order to solve the problems of data tampering, privacy leakage and difficulty in sharing in the traditional student status management system, a method and system architecture of student status management based on blockchain are proposed. The interactive interfaces are provided for users and the underlying data to complete the uploading, modif-ying and query functions. The research results provide theoretical and practical basis for the application of blockchain in student status management.
Human activity recognition in WBAN using ensemble model

Purpose For achieving the profitable human activity recognition (HAR) method, this paper solves the HAR problem under wireless body area network (WBAN) using a developed ensemble learning approach. The purpose of this study is,to solve the HAR problem under WBAN using a developed ensemble learning approach for achieving the profitable HAR method. There are three data sets used for this HAR in WBAN, namely, human activity recognition using smartphones, wireless sensor data mining and Kaggle. The proposed model undergoes four phases, namely, “pre-processing, feature extraction, feature selection and classification.” Here, the data can be preprocessed by artifacts removal and median filtering techniques. Then, the features are extracted by techniques such as “t-Distributed Stochastic Neighbor Embedding”, “Short-time Fourier transform” and statistical approaches. The weighted optimal feature selection is considered as the next step for selecting the important features based on computing the data variance of each class. This new feature selection is achieved by the hybrid coyote Jaya optimization (HCJO). Finally, the meta-heuristic-based ensemble learning approach is used as a new recognition approach with three classifiers, namely, “support vector machine (SVM), deep neural network (DNN) and fuzzy classifiers.” Experimental analysis is performed. Design/methodology/approach The proposed HCJO algorithm was developed for optimizing the membership function of fuzzy, iteration limit of SVM and hidden neuron count of DNN for getting superior classified outcomes and to enhance the performance of ensemble classification. Findings The accuracy for enhanced HAR model was pretty high in comparison to conventional models, i.e. higher than 6.66% to fuzzy, 4.34% to DNN, 4.34% to SVM, 7.86% to ensemble and 6.66% to Improved Sealion optimization algorithm-Attention Pyramid-Convolutional Neural Network-AP-CNN, respectively. Originality/value The suggested HAR model with WBAN using HCJO algorithm is accurate and improves the effectiveness of the recognition.
Modeling of a wheeled humanoid robot and hybrid algorithm-based path planning of wheel base for the dynamic obstacles avoidance

Purpose Most of the conventional humanoid modeling approaches are not successful in coupling different branches of the tree-type humanoid robot. In this paper, a tree-type upper body humanoid robot with mobile base is modeled. The main purpose of this work is to model a non holonomic mobile platform and to develop a hybrid algorithm for avoiding dynamic obstacles. Decoupled Natural Orthogonal Complement methodology effectively combines different branches of the humanoid body during dynamic analysis. Collision avoidance also plays an important role along with modeling methods for successful operation of the upper body wheeled humanoid robot during real-time operations. The majority of path planning algorithms is facing problems in avoiding dynamic obstacles during real-time operations. Hence, a multi-fusion approach using a hybrid algorithm for avoiding dynamic obstacles in real time is introduced. Design/methodology/approach The kinematic and dynamic modeling of a humanoid robot with mobile platform is done using screw theory approach and Newton–Euler formulations, respectively. Dynamic obstacle avoidance using a novel hybrid algorithm is carried out and implemented in real time. D star lite and a geometric-based hybrid algorithms are combined to generate the optimized path for avoiding the dynamic obstacles. A weighting factor is added to the D star lite variant to optimize the basic version of D star lite algorithm. Lazy probabilistic road map (PRM) technique is used for creating nodes in configuration space. The dynamic obstacle avoidance is experimentally validated to achieve the optimum path. Findings The path obtained using the hybrid algorithm for avoiding dynamic obstacles is optimum. Path length, computational time, number of expanded nodes are analysed for determining the optimality of the path. The weighting function introduced along with the D star lite algorithm decreases computational time by decreasing the number of expanding nodes during path generation. Lazy evaluation technique followed in Lazy PRM algorithm reduces computational time for generating nodes and local paths. Originality/value Modeling of a tree-type humanoid robot along with the mobile platform is combinedly developed for the determination of the kinematic and dynamic equations. This paper also aims to develop a novel hybrid algorithm for avoiding collision with dynamic obstacles with minimal computational effort in real-time operations.
Analyzing GAN artifacts for simulating mammograms: application towards finding mammographically-occult cancer

Generative adversarial networks (GANs) can synthesize various feasible looking images. We showed that a GAN, specifically, a conditional GAN (CGAN), can simulate breast mammograms with normal, healthy appearances, and can help detect mammographically-occult (MO) cancer. However, like other GANs, CGANs can suffer from various artifacts, e.g., checkerboard artifacts, that may impact the quality of the final synthesized image, as well as the performance of detecting MO cancer. In this study, we explored the types of GAN artifacts that exist in mammogram simulations and its effect on MO cancer detection. We first trained a CGAN using digital mammograms (FFDMs) of 1366 women with normal/healthy breasts. Then, we tested the trained CGAN on an independent MO cancer dataset with 333 women with dense breasts (97 MO cancer). We trained a convolutional neural network (CNN) on the MO cancer dataset, where real and simulated mammograms were fused, to identify women with MO cancer. We then randomly sampled 50 normal controls and found 11 and 7 cases with checkerboard and nipple artifacts, respectively. The mean and standard deviation score for the trained CNN for the cases with checkerboard and nipple artifacts were low, 0.236 &plusmn; 0.227 with [min, max] = [0.017, 0.761] and 0.069 &plusmn; 0.069 with [min, max] = [0.003, 0.213], respectively, showing the minimal effect of GAN artifacts on MO cancer detection.
Organization, Extraction, Classification and Prediction of Age in Facial Images using Convolutional Neuronal Network

The development of technology and the popularization of AI (artificial intelligence), face recognition has become fundamental by various applications, both in military and economic appearance because it is gradually being introduced into the lives of individuals, exemplifying, in the use of face recognition for unlocking mobile phones. Starting in the 1990s, they began to learn age identification by means of a face photo, it should be said that the recognition of the old face is quite challenging within the environment of the perspective by PC. This article is made in order to be used in marketing, as it could be given differentiated products according to the age of consumers; for this purpose we have used public databases to classify by age the images of faces of both men and women, thanks to a model of Convolutional Neural Network (CNN), with which we obtained an efficiency in the categorization of around 97.86%, we also performed prediction tests in which the silver model obtained an approximate success rate of 87.64%. Keywords— Convolutional Neural Network, age classification, mathematical model, artificial intelligence, CNN.