Reviewerly/exHarmony huggingface dataset:
Infrastructure Platform for Creating and Distributing VR/AR Solutions

The purpose of the work is to develop a solution to increase the number of companies involved in the digital economy markets formation, to assist in accelerating of new products and services based on virtual and augmented reality technology introduction, to ensure their competitiveness and to overcome technological barriers. The authors analyze the current situation in the Russian digital economy, related to the end-to-end technology of virtual and augmented reality, technological barriers that hinder the economy development. The problems reside in low digital literacy, lack of understanding of VR/AR using possibilities in a professional environment, shortage of developers, lack of VR/AR content in consumer and specialized fields, the long alignment process of pilot projects for implementation in state corporations and industrial enterprises, as well as the lack of Russian industry standards for design systems (CAD) and universal VR/AR devices. To address the identified barriers, the authors describe the concept of digital online platform for creating and distributing VR/AR content and services.
Automatic Brain Cancer Segmentation on PET Image

When treating a brain tumor, a doctor needs to know the site and the size of the tumor. Positron emission tomography (PET) can be effectively applied to diagnose such cancers based on the heightened glucose metabolism of early-stage cancer cells. The purpose of this research is to extract the regions of skull, brain tumor, and brain tissue from a series of PET brain images and then a three-dimensional (3D) model is reconstructed from the extracted skulls, brain tumors, and brain tissues. Knowing the relative site and size of a tumor within the skull is helpful to a doctor. The contours obtained by the segmentation method proposed in this study are quantitatively compared with the contours drawn by doctors on the same image set since the ground truth is unknown. The experimental results are impressive.
Image Orientation Detection Using Convolutional Neural Network

Image orientation detection is often a prerequisite for many applications of image understanding and recognition. Recently, with the development of deep learning, mang significant Convolutional Neural Network (CNN) architectures are proposed and widely used in computer vision areas. In order to investigate the performance of CNN on image orientation detection task, in this paper, we first evaluate several famous CNN architectures, such as AlexNet, GoogleNet and VGGNet on this task, then we test a new CNN architecture by combining these networks. We collect six kinds of image, including landscape, block, indoor, human face, mail and natural images, in which the first three ones are regarded as difficult categories of orientation detection by previous work. The experiment results on these datasets indicate the effectiveness of the proposed network on image orientation detection task.
Analysis of Collaborative and Convenient E-Learning on Cloud Computing

The process of learning is rooted from individual human lives, which is further slowly influenced by environment. Learning elevates in the social environment by building an increased capability and ability of learning in individuals. Due to the period of time and generational shift, it uses new approaches. The new generation students use technology on daily basis. In this digital era, students are mostly in exploring new technologies while interacting with a media rich content. Better learning tools should be provided by higher education for off campus and on-campus learning to support self-paced, independent and personalized environment for next generation students. Generational shifts of learning paradigm should be addressed by colleges and universities for deploying an effective teaching and learning process. The explosion of information technology can fulfil the new learning demands in an effective and efficient way. The adoption of cloud services for educational operations will enable higher education authorities to fulfill their infrastructure needs with less investment. In case of cloud computing (CC), software purchasing, installation, and maintenance processes are eliminated.
Data-to-text Generation with Pointer-Generator Networks

According to attention mechanism-based encoder-decoder model for data-to-text generation does not fully consider the structure information of the slot-value pair data and the integrity of the expression. In this paper, firstly, the pointer-generator network is used to copy the word from the input to reference the slot-value pairs data and handle the problem of the out of vocabulary words and rare words. Then propose the slot-attention mechanism to calculate the attention score using the attribute sequence and value sequence simultaneously, construct the context vector separately for the attribute sequence and the value sequence, and input them into the decoder to alleviate the problem of assigning values to the wrong attributes. Secondly, this paper introduces the coverage mechanism to use the historical attention information to calculate the attention score so that the model considers more about the unexpressed attributes to alleviate the situation where some attributes appear repeatedly while the others do not appear in the generated text. Finally, the slot-value pair data are content words, which are not aligned one-to-one with the generated text. This paper uses the attention distribution gate to dynamically control the softness of the attention distribution. When generating a content word, the model focus on the most relevant one word to capture the semantic information, attention distribution becomes more harder; When generating a function word, the model focus on the most relevant several words to capture the syntactic information, and the attention distribution becomes smoother. Experimental results on the E2E dataset demonstrate that the proposed model can further improve the quality of the generated text.
Meteorological Indigenous Knowledge Systems for Prediction of Rainfall in the Chimanimani District of Zimbabwe and Potential for Community Disaster Preparedness

The paper focuses on indigenous knowledge systems (IKS) which people of Chimanimani District of Zimbabwe use to predict rainfall patterns.This is based on a 2019 socio-ecological study conducted in the district.The main objective of the study was to establish the extent of use and efficacy of meteorological IKS for prediction of rainfall in Chimanimani and to explore potential contribution of the meteorological IKS in local community disaster preparedness.The study came at the backdrop of a trail of unprecedented human and environmental destruction that occurred in the district following the devastating Cyclone Idai of April of 2019.Despite the existence of IKS and modern weather forecasting tools, the Cyclone seemed to have caught both the community and government unprepared, prompting this study.In depth-interviews and storytelling were conducted with traditional leaders and community elders.A qualitative approach was applied through the use of detailed interviews and focus group discussion to collect meteorological IKS from three communities in the district.Thematic analysis anchored in the interpretive and ontological paradigms was utilized within the realism theory.The meteorological IKS found in the study were classified into five interpretive categories namely vegetation, animals, birds, insects and cosmology.The study concluded that meteorological IKS are no longer widely used due to a number of factors including inferioritisation and limited documentation.It was noted that the IKS have potential for incorporation into the community disaster preparedness frameworks.
Cloud load balancing: Blockchain deployment at integrated DopCloud synthesis on Healthcare data

The newer transformations happen on every fold at outsourcing load balancing in the cloud has taken a steep situation. The deploying blocks using the design of distribution record amongst all the servers in finding the deeper route to release the data in time consent format without disturbing the working background modules of the record basis at state machine and all the external information can be inserted and assessed. Cloud load balancing is the distribution between the workloads and computational resources manageably allowing workload demands into the enterprise environment by allocating in different servers or networks or computers. The DopCloud procedure are discussed by analyzing and implementing the dynamic optimization of packet flow routing on the cloud through the hardest deployment of huge data load to balance by consuming VNF and transition minute stack layers. Every deployment face synthesis on changing servers along with the client codes to infuse the variant trustworthy between load balancing and cloud backup timeline on the stipulated time. In Healthcare it began with the data losses when EHR has to stop to take its place for the data storage abruption, the changes made available for the data load of the patient's diagnosis are mantled over a group of stacks to control the cloud network. Here, the RIFT and VNF cloud provides richer protocol with a level of certainty and promising to the big data load to be secure by the algorithm made. Mostly, planning's of the state machine over containerization is to be deposited onto the packet flowing route optimization (DOPER) and the communication is kept out of reach when the contracts made are available to every peer node in the DopCloud.
Vision-Based Autonomous Navigation with Evolutionary Learning

In this paper, we propose a vision-based autonomous robotics navigation system, it uses a bio-inspired optical flow approach using the Hermite transform and a fuzzy logic controller, the input membership functions were tuned applying a distributed evolutionary learning based on social wound treatment inspired in the Megaponera analis ant. The proposed method was implemented in a virtual robotics system using the V-REP software and in communication con MATLAB. The results show that the optimization of the input fuzzy membership functions improves the navigation behavior against an empirical tuning of them.
Machine Learning in Quantum Communication

Phase estimation of signals below noise is a main challenge in optical quantum communication receivers. Heterodyne detection at a SNR of less than -20 dB with machine learning based optimized carrier phase estimation is demonstrated.
Scalable and Parallel Deep Bayesian Optimization on Attributed Graphs

We propose a general and scalable global optimization framework directly operating on annotated graph data by introducing a Bayesian graph neural network to approximate the expensive-to-evaluate objectives. It prevents the cubical complexity of Gaussian processes and can scale linearly with the number of observations. Its parallelized variant makes it scalable. We provide strict theoretical support on its convergence. Intensive experiments conducted on both artificial and real-world problems, including molecular discovery and urban road network design, demonstrate the effectiveness of the proposed methods compared with the current state of the art.
Research on LSB-based Digital Image Information Camouflage Algorithm

Considering the security problem of LSB-based information camouflage, this paper analyzes and verifies the effect of different bit planes on the effect of hidden information, and aiming at the characteristics of large data volume and high redundancy, the last-two random embedding method is proposed to camouflage information, which improves the LSB algorithm. The concealment amount of information camouflage is increased in practice. For different information embedding methods, this paper analyzes and verifies the information camouflage algorithm based on improved LSB have a greater amount of information hiding while ensuring security.
Feature-Oriented Control Programming

Managing variability in control programs often requires code duplication or that all variants are anticipated in advance. In this paper, we present a new approach to obtaining modular functionality reuse across variants. Using the language mechanisms diagram inheritance and connection interception, a feature model and an interactive feature-selection wizard can be automatically derived from the control program.
Colour Image De-noising Analysis Based on Improved Non-local Mean Filter

In a non-linear filter, open resources filter is a particular scenario that is used to reduce the Gaussian noise in our paper and it performs well to reduce it. The major advantage of non-local means filter is to preserve the limits and particulars of a unique image. In this paper, combined both open means filter and mutual filter to recommend an enhanced filter for colour picture de-noising. Novel influence significance is computed by addition consistency in sequence into the weight to evaluate the parallel of the patch. At the final stage of this paper deals that the proposed method of NLM and BILF is a suitable method to reduce the Gaussian sound and combination of sound.
Predicting Student Flight Performance with Multimodal Features

This paper investigates the problem of predicting student flight performance in a training simulation from multimodal features, including flight controls, visual attention, and knowledge acquisition tests. This is a key component of developing next generation training simulations that can optimize the usage of student training time. Two types of supervised machine learning classifiers (random forest and support vector machines) were trained to predict the performance of twenty-three students performing simple flight tasks in virtual reality. Our experiments reveal the following: 1) features derived from gaze tracking and knowledge acquisition tests can serve as an adequate substitute for flight control features; 2) data from the initial portion of the flight task is sufficient to predict the final outcome; 3) our classifiers perform slightly better at predicting student failure than success. These results indicate the feasibility of using machine learning for early prediction of student failures during flight training.
Implementation of Apriori Algorithm Data Mining for Increase Sales

Any company or organization that wants to survive needs to determine the right business strategy. The product sales data carried out by Lakoe Dessert Pondok Kacang will eventually result in a pile of data, so it is unfortunate if it is not re-analyzed. The products offered vary with a wide variety of products as many as 45 products, to find out the products with the most sales and the relationship between one product and another, one of the algorithms is needed in the data mining algorithm, namely the a priori algorithm to find out, and with the help of the Rapidminer 5 application, with a support value 2,4% and a confidence value 50%, products that customers often buy or are interested in can be found. This study used sales data for March 2020, which amounted to 209 transaction data. From the research, it was found that the item with the name Pudding Strawberry and Pudding Vanilla was the product most purchased by consumers. With knowledge of the most sold products and the patterns of purchasing goods by consumers, Lakoe Dessert Pondok Kacang can develop marketing strategies to market other products by analyzing the profits from selling the most sold products and anticipating running out or empty of stock or materials at a later date.
Clustering Methods Analysis in the E-Learning

In this paper, the analysis of students who were part of the distance learning program has been done using various educational data mining clustering algorithms on the basis of their performance and activities carried out as a part of the process. Clusters have been used to find the relation between attributes (activities involved in the E-learning process such as downloading of study related material, exchange of messages with tutors and colleagues and posting in the discussion forum) and the final grades of students, and to find which characteristics mostly affect the classes. To sort out this, we have used various data mining techniques such as Agglomerative Hierarchical Clustering (using Ward method), and Non-Hierarchical clustering methods such as KMeans, KMeans ++, and CMeans The implementation of these algorithms is done in R language. The comparison of Hierarchical and non-Hierarchical clustering methods is made to find the most efficient algorithm.
Knowledge Inference Model of OCR Conversion Error Rules Based on Chinese Character Construction Attributes Knowledge Graph

OCR is a character conversion method based on image recognition. The complexity of the character and the image quality plays a key role in the conversion accuracy. The OCR conversion process has the characteristics of irregular conversion errors and the combination between incorrect conversion words and context of original location in certain text scenarios is established in semantic. In this paper, we propose an OCR conversion error rules inference model based on Chinese character construction attribute knowledge graph to analyze and inference the structure and complexity of Chinese characters. The model integrates a variety of coding methods, extracts features of entities and relationships of different data types with different encoder in the knowledge graph, uses convolutional neural networks to learn and inference the unknown error rules in the OCR conversion. In addition, in order to enable the triple feature matrix to fully contain the construction attribute information of the Chinese characters, a feature crossover algorithm for feature diffusion of the triple feature matrix is introduced. In this algorithm, the relation matrix and the entities matrix are crossed to generate the new feature matrix which can better represent the triple of knowledge graph. The experimental results show that, compared with the current mainstream knowledge inference model, the OCR conversion error rules inference model incorporating the feature cross algorithm has achieved important improvements in MRR, Hits@1, Hits@2 and other evaluation indicators on public data sets and task-related data sets.
Manifold Learning Based on Straight-Like Geodesics and Local Coordinates

In this article, a manifold learning algorithm based on straight-like geodesics and local coordinates is proposed, called SGLC-ML for short. The contribution and innovation of SGLC-ML lie in that; first, SGLC-ML divides the manifold data into a number of straight-like geodesics, instead of a number of local areas like many manifold learning algorithms do. Figuratively speaking, SGLC-ML covers manifold data set with a sparse net woven with threads (straight-like geodesics), while other manifold learning algorithms with a tight roof made of titles (local areas). Second, SGLC-ML maps all straight-like geodesics into straight lines of a low-dimensional Euclidean space. All these straight lines start from the same point and extend along the same coordinate axis. These straight lines are exactly the local coordinates of straight-like geodesics as described in the mathematical definition of the manifold. With the help of local coordinates, dimensionality reduction can be divided into two relatively simple processes: calculation and alignment of local coordinates. However, many manifold learning algorithms seem to ignore the advantages of local coordinates. The experimental results between SGLC-ML and other state-of-the-art algorithms are presented to verify the good performance of SGLC-ML.
Quantum Two-Mode Squeezing Radar and Noise Radar: Correlation Coefficient and Integration Time

Quantum two-mode squeezing (QTMS) radars and noise radars perform matched filtering of the received signal using a reference signal stored within the radar. Their target detection performance depends on the correlation between the received and reference signals, and the improved detection performance of QTMS radars over noise radars is due to the fact that it has an increased correlation. In this paper, we present a novel way of understanding how detection performance depends on the correlation by relating the correlation to the integration time. Concretely, we prove that increasing the correlation by a given factor is equivalent to increasing the integration time by the square of that factor. We apply this result to a recent QTMS radar experiment where the increase in correlation was approximately a factor of 3. This was found to be equivalent to increasing the integration time by a factor of 9, thus confirming the validity of our result and approximation.
CycleGAN-Based Image Translation for Near-Infrared Camera-Trap Image Recognition

Due to its invisibility, NIR (Near-infrared) flash has been widely used to capture the images of wild animals in the night. Although the animals can be captured without notice, the gray NIR images are short of color and texture information and thus is difficult to analyze, for both human and machine. In this paper, we propose to use CycleGAN (Generative Adversarial Networks) to translate NIR image to the incandescent domain for visual quality enhancement. Example translations show that both color and texture can be well recovered by the proposed CycleGAN model. The recognition performance of a SSD based detector on the translated incandescent images is also significantly better than that on the original NIR images. Taking Wildebeest and Zebra for example, an increase of \(16\%\) and \(8\%\) in recognition accuracy has been observed.
Quantifying object similarity: Applying locality sensitive hashing for comparing material culture

We present a novel technique that compares and quantifies images used here to compare similarities between material cultures. This method is based on locality sensitive hashing (LSH), which uses a relatively fast and flexible algorithm to compare image data and determine their level of similarity. This technique is applied to a dataset of sculpture faces from the Aegean, Anatolia, Cyprus, Egypt, Iran, Indus/Gandhara, the Levant, and Mesopotamia. Results indicate that the objects can be differentiated based on regional differences and show similarities to other locations that share specific material culture traits. Images from known locations enable a network of compared objects to be constructed, where inverse closeness centrality and link weights are used to indicate areas that have a greater or less cultural similarity to other regions. Different periods are assessed, and the results demonstrate that objects from earlier than the 9th century BCE show greater similarity to other local and Egyptian items. Objects from between the 9th and 4th centuries BCE increasingly show interregional similarity, with the eastern Mediterranean, including the Aegean, Anatolia, Egypt, and Cyprus, having close similarity to multiple regions. After the 4th century BCE, greater sculptural similarity is found across a wide area, including the Aegean, Cyprus, Egypt, Mesopotamia, and Gandhara. In general, sculptures from more distant areas increase in similarity in later periods, that is starting from the 9th century BCE. The results demonstrate that the technique can be applied to quantifying object similarity and extended to a broad range of archaeological objects, while also being a tool for rapid analysis that requires minimal data compared to some machine learning techniques. The code and data are provided as part of the outputs.