Reviewerly/exHarmony huggingface dataset:
ABCD: Analogy-Based Controllable Data Augmentation

We propose an analogy-based data augmentation approach for sentiment and style transfer named Analogy-Based Controllable Data Augmentation (ABCD). The object of data augmentation is to expand the number of sentences based on a limited amount of available data. We are given two unpaired corpora with different styles. In data augmentation, we retain the original text style while changing words to generate new sentences. We first train a self-attention-based convolutional neural network to compute the distribution of the contribution of each word to style in a given sentence. We call the words with high style contribution style-characteristic words. By substituting content words and style-characteristic words separately, we generate two new sentences. We use an analogy between the original sentence and these two additional sentences to generate another sentence. The results show that our proposed approach decrease perplexity by about 4 points and outperforms baselines on three transfer datasets.
Applied research on higher English Teaching under the background of artificial intelligence

During a period of artificial intelligence, all walks of life make full use of Internet technical knowledge to achieve industry upgrading and transformation, and start the “artificial intelligence” mode. Recently the growth of artificial intelligence technical knowledge is becoming increasingly mature, especially in English teaching. College English teaching should seize the opportunity brought by artificial intelligence technical knowledge to realize the improvement of professional courses. And the modern teaching status derived from artificial intelligence and the traditional teaching status will be effectively integrated to build an efficient hybrid teaching mode, which promotes the reform and advancement of English education and the modernization process. This article starts from the disadvantages of higher English education mode, analyzes the opportunities that artificial intelligence technology brings to higher English teaching, and introduces the connotation of artificial intelligence technology. Then this article studies the development of Al technical knowledge in English education, and finally investigates the application of Al technical knowledge from many aspects.
Text Data Compression Software Design Using Arithmetic Coding Method

Data compression is the science or art of representing information in a more compact form. Various algorithms have been developed for data compression purposes. One of the algorithms used is Arithmetic Coding. The working principle of this algorithm is to replace a series of input symbols with floating point numbers, where the longer and more complex the encoded message, the more bits required for file compression. This software is designed using the Visual Basic 6.0 programming language and is able to compress text files with a compression rate of about 75 percent. This system can also be used to compress two or more text files at once.
Implementasi Metode K-Nearst Neighbor Berbasis Euclidean Distance Untuk Klasifikasi Penerimaan Vaksin Covid-19

The first semester of 2020 the COVID-19 pandemic took place, resulting in many countries implementing massive social restrictions. And one of the solutions provided by the government is through the regulation of the Minister of Health of the Republic of Indonesia number 84 of 2020 regarding the implementation of vaccinations in the context of dealing with the 2019 corona virus disease pandemic. This vaccine is expected to be one of the solutions to prevent transmission and prevent the risk of spreading COVID-19. This study aims to design and build an application for the classification of vaccine recipients based on their urgency according to their priority scale based on predetermined criteria and sub-criteria. Vaccination is given based on the order where priority is given to the frontline who are dealing directly with the COVID-19 pandemic. This research implements the K-Nearest Neighbor method based on Euclidean distance. This method was chosen because it is simple, easy to learn and effective in determining distance. In the process of classifying vaccine recipients, several criteria are needed, namely gender, occupation, age, pregnancy and medical history based on the results of observations and interviews that have been carried out. Then in determining the weight value for each criterion/attribute, and a ranking process is carried out which results in a decision to be used as a recommendation for the recipient of the covid-19 vaccine. Keywords: Vaccine , Covid-19, K-Nearest Neighbor , Euclid e an distance
Secured Cloud Data Transmission using Cross ABE Algorithm

Cloud security is becoming more essential than ever with the tremendous development of delicate cloud data. The cloud information and services are located in massively scalable data centers and can be accessed anywhere. Unfortunately, the development of cloud users has been followed by an increase in cloud malicious activity. More and more vulnerabilities are being found, and fresh safety advisories are being released almost every day. Millions of customers surf the cloud for different reasons, so they need extremely secure and persistent services. The cloud storage system interconnect with the a load of potential security risks. So the cross encryption of Ciphertext Policy Attribute Based Encryption (CPAB) and Key Policy Attribute-based encryption algorithm which increases the security level in the encryption side. A segmentation part helps in splitting the encrypted file in storing the data in the cloud side, the Desegmentation part in the receiver side can easily combines spitted data into the single file for validation examine an authentication level in the received data. Here the cloud storage easily with the file fragmentation processes. This processes research over the storing mass amount of data on off- site installation, which can eliminate the cost in maintaining the physical hardware. Cloud's future includes a much greater degree of privacy and authentication, particularly in extending the variety of apps. We suggest a straightforward data protection model where data is encrypted before it is introduced in the cloud using key policy attribute-based encryption to ensure data confidentiality and safety. The storing data is the most common application for the cloud server.
THEORETICAL CONSIDERATIONS REGARDING VIRTUAL METHODS TO OBTAIN THE GARMENT PROTOTYPE (2D/3D AND 3D/2D)

With the progress and development of science and technology, virtual reality technology is becoming more and more present, its application in digital creations is becoming more and more widespread. Everything the user does is calculated by the computer and has real-time feedback, making this virtual environment more realistic and giving the user an immersive experience. This technology integrates the latest developments in computer science, computer simulation, artificial intelligence, recognition, display and online parallel processing. Virtual prototyping offers a new way to explore the design and subsequent changes that need to be made to the product before it is manufactured in larger quantities or put into production. The development of new product models using virtual tools requires a certain level of training of the designer; he/she must have skills in understanding and using the specific tools for CAD systems (2D and 3D) and technical knowledge regarding the ways of conceptual product development. This paper presents the methodology of the virtual development process for an apparel product model, using the tools of the 2D and 3D digital environments. The first solution is to design the 2D patterns of the components of an apparel product, followed by a 3D simulation that verifies the aforementioned solution. The second solution is to design the 3D components using the tools of the 3D digital environment (the software used is Clo3D) and then extract the 2D parts required for the classical manufacturing process.
AI-ML based NLP Development using SVM & RF concepts

In this paper, a short survey of the comment based Natural Language Processing System utilizing semi-managed bootstrapping, ML approaches of Support Vector Machine (SVM) and Random Forest (RF) strategy is being introduced basically.How to cite this article:Jipeng T, Neelagar MB, Prasad AN et al. AI-ML based NLP Development using SVM and RF Concepts. J Adv Res Appl Arti Intel Neural Netw 2020; 4(2): 1-2.
USE OF AIRBORNE GEOPHYSICS, REMOTE SENSING AND GEOLOGICAL MODELLING TO ASSIST AND UPGRADE 1:100,000 SCALE GEOLOGICAL MAPPING

Geological maps are a 2D geological image on an undulating terrain slice through a 3D geological model. Systematic national geological mapping by Federal and State geological surveys at progressively finer scales (1:250,000 to 1:100,000 to 1:25,000 etc.), most of it carried out with traditional methods (air photo interpretation, ground mapping etc.), underpins earth resource exploration, exploitation and management. In Australia total 1:250,000 and partial 1:100,000 scale coverage as well as selected mineral province geological mapping at finer scales, has been achieved. Australia now has the benefit of nation-wide detailed to semi-detailed coverage with airborne geophysics (magnetics, radiometrics, airborne EM), gravity, magneto-tellurics, reconnaissance crustal seismic, SAR DTM, Landsat, ASTER and other remote sensing data. Most existing mapping: - was derived prior to the acquisition of, or without analysis / interpretation of, this geophysical and remote sensed information with the range of modern geophysical analysis and geological modelling software to value add the geophysical information for input to the geological mapping process; and precedes the recent rapid evolution of understanding of geological processes and capacity for 3D geological mapping. Results from a desk-top case study in semi-arid terrain from Western Australia, show the benefits and workflow, of analysis of available geophysical and remote sensed data for expanding / improving 1:100,000 scale geological mapping, using existing 1:250,000 and neighboring 1:100,000 mapping and explanatory notes as preliminary ground truth, to guide on-ground 1:100,000 mapping and provide a resource mapping perspective. The workflow, utilising Intrepid, (ERDAS Imagine, ER Mapper, ArcGIS) or QGIS and GeoModeller software includes: - SAR DTM, radiometrics, Landsat and ASTER classification of terrain – pseudo-geomorphology, preliminary outcrop lithology identification and inference of micro-seismicity and recent fault reactivation (lineament analysis); Magnetic depth estimates and “worm” analysis for structural analysis and 3D modelling as well as definining pre-Cainozoic. "under-cover" basement fabric; Where gravity coverage justifies it, gravity modelling over selected areas; Airborne EM regional reconnaissance lines 1D or 2.5D inversion and if available assessment of available magneto-telluric data and crustal seismic reflection data; and As justified, 3D geological modelling to construct or improve selected key geological sections.
3D Neural Scene Representations for Visuomotor Control

Humans have a strong intuitive understanding of the 3D environment around us. The mental model of the physics in our brain applies to objects of different materials and enables us to perform a wide range of manipulation tasks that are far beyond the reach of current robots. In this work, we desire to learn models for dynamic 3D scenes purely from 2D visual observations. Our model combines Neural Radiance Fields (NeRF) and time contrastive learning with an autoencoding framework, which learns viewpoint-invariant 3D-aware scene representations. We show that a dynamics model, constructed over the learned representation space, enables visuomotor control for challenging manipulation tasks involving both rigid bodies and fluids, where the target is specified in a viewpoint different from what the robot operates on. When coupled with an auto-decoding framework, it can even support goal specification from camera viewpoints that are outside the training distribution. We further demonstrate the richness of the learned 3D dynamics model by performing future prediction and novel view synthesis. Finally, we provide detailed ablation studies regarding different system designs and qualitative analysis of the learned representations.
A Critical Survey on Privacy Prevailing in Mobile Cloud Computing: Challenges, State of the Art Methods and Future Directions

With the explosive growth of mobile applications and extensive praxis of cloud computing, mobile cloud computing has been introduced to be a potential technology for mobile services. But privacy is the main concern for a mobile user in the modern era. In the current study, we address the privacy challenges faced by mobile users while outsourcing their data to the service provider for storage and processing. However, a secure mobile user is required to protect these fundamental privacy factors such as their personal data, real identity, current location and the actual query sent to the cloud vendor server while availing different cloud services. Under these privacy metrics, we evaluated the existing approaches that are counting privacy challenge in mobile cloud computing. The primary focus of this study is to presents a critical survey of recent privacy protection techniques. Leading to objective, the current study conduct a comparative analysis of these state of the art methods with their strong points, privacy level and scalability. After analysis, this paper suggests the pseudo-random permutation method could be a promising solution that can be taken into consideration for preserving user personal information and data query privacy in MCC more efficiently. Primarily, the purpose of the survey was to focus on further advancements of the suggested method. Furthermore, we present the future research directions in the mobile cloud computing paradigm
Causal Reasoning Model Based on Medical Knowledge Graph for Disease Diagnosis

In view of the non-interpretability of disease diagnosis models based on deep learning, a knowledge reasoning model based on medical knowledge graph for intelligent diagnosis is proposed. Given the patient symptom set, the co-occurrence of the patient and the disease is calculated, then the patient suffering from one disease is calculated. Based on the dynamic threshold value, the final disease diagnosis result of the patient is outputted. According to the symptoms of patients and the symptoms in the knowledge graph, the causal reasoning of the disease diagnosis is interpretable. Experiments on 145,712 pediatric electronic medical records in Chinese show that the proposed model can predict diseases with interpretability, and the accuracy reaches-82.12%.
<i>Zoom‐fwd</i>: Efficient technique for 3D gestual interaction with distant and occluded objects in virtual reality

Abstract In the last decade, there has been an extraordinary acceleration in immersive technologies, including virtual reality (VR) and augmented reality (AR). The VR interfaces have widely explored various 3D interaction‐based approaches. 3D interaction is one of the main features of any VR system. However, the problems while selecting and manipulating distant and occluded 3D virtual objects in VR are still unresolved and suffer from a lack of precision and accuracy. To interact with VR/AR systems, various devices such as VR headsets or gloves are used. These devices are not very reliable yet; in fact they are inconvenient and invasive to a person's comfort. Currently, the natural user interfaces (NUIs) are increasingly introduced in human computer interaction (HCI) systems as gestures recognition, which prove to be very useful in the improvement of the user engagement and presence sensing. More particularly, they provide more user‐friendly and nonintrusive 3D interaction methods and techniques. In this article, the Zoom‐fwd , which is an efficient 3D interaction technique is presented. The proposed technique uses gesture recognition for different 3D interaction tasks like selection and manipulation. This new approach allows an efficient interaction with distant and occluded objects, while providing a precise selection, even when the environment is crowded. The Zoom‐fwd technique is a software solution for a number of hardware and software problems. A user study is conducted to determine whether the proposed technique is more suitable when performing interaction tasks. The results show that the Zoom‐fwd technique provides effective interaction with distant and occluded objects, by improving the user task completion performance. In addition, this indicates that selection precision has been enhanced significantly with the Zoom‐fwd technique.
Cutting L1-Norm Distance Discriminant Analysis with Sample Reconstruction

Dimensionality reduction plays an important role in the fields of pattern recognition and computer vision. Recursive discriminative subspace learning with an L1-norm distance constraint (RDSL) is proposed to robustly extract features from contaminated data and L1-norm and slack variables are utilized for accomplishing the goal. However, its performance may decline when too many outliers are available. Moreover, the method ignores the global structure of the data. In this paper, we propose cutting L1-norm distance discriminant analysis with sample reconstruction (C-L1-DDA) to solve the two problems. We apply cutting L1-norm to measure within-class and between-class distances and thus outliers may be strongly suppressed. Moreover, we use cutting squared L2-norm to measure reconstruction errors. In this way, outliers may be constrained and the global structure of data may be approximately preserved. Finally, we give an alternating iterative algorithm to extract feature vectors. Experimental results on two publicly available real databases verify the feasibility and effectiveness of the proposed method.
Utilisation of Deep Learning-Enabled Image Analysis for Detection and Enumeration of Plasmodial Forms in Red Cells

Abstract Introduction/Objective Objective: This work investigates utilisation of deep learning enabled peripheral blood smear image analysis for automated detection and enumeration of red cell parasites. Methods/Case Report Methods: Peripheral blood smear red cell images from 30 individuals identified as positive and/or negative for plasmodilum falciparum forms were used. Blood submitted to hematology laboratory for complete blood count was evaluated on Sysmex XN 3100 analyzer with routine peripheral blood slides performed on instrument associated SP50 stainer. Images of red cells obtained with Xfinity DX40 microscope mounted camera were subjected to classification using deep learning software (Cognex, ViDi Suite 4.1). The training of the classification tool was performed on 200 peripheral blood smear images divided in two dataset classes: normal/negative and abnormal/positive for plasmodia, 50% of training images represented positive data set. Performance of the developed model was tested on 300 images including 66% positive for plasmodia obtained from 20 patients. Enumeration of parasitic forms was performed for each case. Model performance was compared to expert hematopathology reviewer which was used as gold standard. Results (if a Case Study enter NA) Results: Overall, Cognex ViDi Suite 4.1 demonstrates the effectiveness in discriminating between images positive and negative for red cell plasmodial forms as well as enables parasite quantification. Following performance specifications were determined for parasite detection: sensitivity (0.969230769), specificity (0.99383217). High correlation coefficients (0.9961) between automatically detected parasites and ground truth, on both image level and patient level, demonstrate the practicality of our method. Conclusion Deep learning enabled image analysis of peripheral blood smears is a promising alternative to manual identification and enumeration of red cell plasmodial forms with performance comparable to expert hematopathology reviewer.
THEATRICAL ART IN PRACTICE OF THE MODERN PRIMARY SCHOOL

The article deals with the important issue in terms of nowadays education, namely the application of theatrical arts in practice of modern primary school. It is explained the concepts of theatrical art, theatrical activities, theatrical games at school; the main types of theatrical performance are described in the article.The aim of the article is to consider the most commonly used methods of dramatization and to prove the relevance of their use in reading lessons. On the basis of analysis and systematization of scientific literature relating to the application of theatrical art at lessons in primary school it is defined that theatrical activities serve as a source of children’s harmonious aesthetic development. Analytical and synthetic nature of theatrical art is the quality that helps to influence the emotional and sensory sphere of a child. Besides, theatrical art resembles children’s games during which it comes natural for children to show their character. The collective nature of theatrical activities intensifies children’s emotions and feelings such as inspiration, joy, a sense of unity for the common task. It is stated that theatrical art promotes the forming of important life values of primary school children, provides an opportunity not only for aesthetic perception, appreciation of beauty, but, above all, active aesthetic activity, which creates a creative, energetic, diligent personality, full of optimism and inspiration. It is proved that theatrical activities should be applied both at lessons and in extracurricular activities. The use of dramatization elements helps a teacher to move away from the imperative and authoritarian forms of communication and to establish trust-based relationships in the process of collaborative art. The use of theatrical means gives the opportunity to activate the emotional sphere of children, stimulate the development of their abilities, expand knowledge of certain topics, bring children a sense of energy and enthusiasm, especially for those for whom the theoretical way of understanding a subject is difficult.
APLICACIÓN DE LA METODOLOGÍA SCRUM-PROGRAMACIÓN EXTREMA AL SISTEMA DE CONTROL DE CONSUMO DE COMBUSTIBLES: APLICACIÓN DE LA METODOLOGÍA SCRUM-PROGRAMACIÓN EXTREMA AL SISTEMA DE CONTROL DE CONSUMO DE COMBUSTIBLES

The methodologies for software development arise when there is a need to use a series of procedures, techniques, tools and documentary support when developing a product. The development of quality software was affected by the unreal planning of managers to estimate time, effort and system costs. The present work aims to present the application of the SXP hybrid methodology in the design of a Fuel Consumption Control System. The phases and workflows of the methodology were described, as well as the iterations for the delivery of the functional requirements and the main artifacts generated user stories, engineering tasks and acceptance tests during the software life cycle.
Attention mutual teaching network for unsupervised domain adaptation person re-identification

Person re-identification (ReID) is an important task in computer vision. Most methods based on supervised strategies have achieved high performance. However, performance cannot be maintained when these methods are applied without labels because styles in different scenes exhibit considerable discrepancy. To address this problem, we propose an attention mutual teaching (AMT) network for unsupervised domain adaptation person ReID. The AMT method improves the performance of a model through iterative clustering and retraining. Meanwhile, two attention modules can teach each other to reduce clustering noise. We conduct extensive experiments on the Market-1501 and DukeMTMC-reID datasets. The experiments show that our approach performs better than state-of-the-art unsupervised methods.