Reviewerly/exHarmony huggingface dataset:
Migration of eHealth Cloud to SeHealth Cloud

Electronic healthcare (eHealthcare) systems are becoming increasingly driven to outsourcing their complicated eHealth cloud data management systems under the growing cloud computing paradigm. Whereas the data can include medical records, the cloud services cannot be completely safe. Secured eHealthcare (SeHealthcare) cloud data is a promising method of protecting the confidentiality of health data, although it can be difficult to conduct successful analyses over security of data. We planned to enhance secrecy into e-healthcare with the support of private cloud even though the transmission between client and clinician to prevent from security attacks.
Class Diagram Generation from Text Requirements: An Application of Natural Language Processing

The software development scheme starts with identifying the requirements for the analysis. The requirements’ process levels begin from analyzing the requirements to sketching the program’s design, which is an essential task for programmers and software engineers. The motivation behind this task is related to the designations of software requirements created in the natural language (NL). To minimize these errors, we can transfer the software requirements to a computerized form using the UML diagram. This research proposes a new method to offer a UML class version from software program specifications using natural language processing (NLP) techniques. The proposed method presents the class diagram in a known configuration and figures out the relationship between the instruction and the class diagram. In this chapter, we aim to enhance the procedure of producing the UML diagrams by utilizing the natural language, which will help the software developers to analyze the software requirements with fewer errors and in an efficient way. The proposed method produced more reliable results compared to the existing techniques available. The proposed approach provides a better analysis of the given requirements and delivers better diagrams while also helping the software engineers.
Learn from one image: Dynamic One-shot learning based on parameter generation

State-of-the-art deep learning algorithms are usually pre-trained on datasets containing millions of images. Adding new classes to these pre-trained networks, require large number of images for each of the new classes. Formulation of such large scale datasets usually require a lot of effort and time. The aim of this paper is to develop novel deep learning based one-shot learning framework which can achieve state-of-the-art results on new classes (one-shot classes) which have only one image each during the training phase. Adding these new one-shot classes, should not degrade the performance of the model on pre-trained classes. Multi-layer transformation function has been proposed in this paper for one-shot learning, where activations of a class are converted to their corresponding parameters. The model is pre-trained on large-scale base classes and the model adapts to new classes with zero training. Experiments were conducted on opensource datasets like MiniImageNet and Pascal-VOC using Nvidia K80 GPU. The model achieves an accuracy of 93.14% for large scale base classes and 64.69% for one-shot classes which is more than 3% better than the current state-of-the-art models.
Cross-Lingual Transfer Learning Framework for Program Analysis

Deep learning-based techniques have been widely applied to program analysis tasks, in fields such as type inference, fault localization, and code summarization. Hitherto deep learning-based software engineering systems rely thoroughly on supervised learning approaches, which require laborious manual effort to collect and label a prohibitively large amount of data. However, most Turing-complete imperative languages share similar control- and data-flow structures, which make it possible to transfer knowledge learned from one language to another. In this paper, we propose a general cross-lingual transfer learning framework PLATO for program analysis by using a series of techniques that are general to different downstream tasks. PLATO allows Bert-based models to leverage prior knowledge learned from the labeled dataset of one language and transfer it to the others. We evaluate our approaches on several downstream tasks such as type inference and code summarization to demonstrate its feasibility.
Tapasqa - Question Answering on Statistical Plots Using Google Tapas

Question answering systems are used to generate answers for the questions proposed. They have been used in various applications like personal assistance and health care. This research aims to build a question answering system for statistical charts. Charts are useful in representing information in a concise and simple manner. But to answer simple questions from the charts is highly complex for a machine. We have built a state-of-the-art model that helps us address open-ended questions and Yes/No binary questions. The model consists of four stages, namely, Plot Element Detection (PED), Optical Character Recognition (OCR), semi-structured table generation and table Question-Answering (QA) stage. The input image is processed in the PED stage to generate bounding box predictions and using that the text data is recognized via the OCR stage. Various plot elements are mapped to the text data recognized to produce a semi-structured table. This table along with the questions are given to the table QA stage. Google TAble PArSing (TAPAS) is employed to answer open-ended questions and TAPAS trained on TabFact dataset is used to answer binary questions. Additional helper functions are added to these models to improve the results. PED stage is trained on Faster R-CNN and Resnet-101 models. Average Precision (AP) for different plot elements were computed after training the model for two lakh iterations. Table QA stage was tested on PlotQA dataset and evaluation was performed on 2k images per category of horizontal, vertical, line and dot plots. The results are better compared to the PlotQA model, which is the original model developed for the dataset. Employing Google Tapas has improved the results and proved to be better than other state of the art models.
Possible actions detached from the formation of the other: culture from the framework of education

sense.
Cavity Quantum Electrodynamics with Laser-Cooled Atoms and Optical Nanofibers

A cavity quantum electrodynamics (QED) system, which consists of an atom and photons confined in a cavity, is one of the most basic hybrid systems. In the strong coupling regime of cavity QED, quantum interaction between atoms and photons manifests itself, and it becomes possible to generate, manipulate, and measure quantum states of atom and light. Therefore, a cavity QED system is an ideal testbed for investigating quantum nature of atom and light. Recently, efforts have been made toward realization of a quantum network by connecting multiple cavity QED systems by optical fibers. However, it is technically challenging to connect a large number of conventional Fabry–Perot cavities with high efficiency. Here, we review novel all-fiber cavity QED systems based on optical nanofibers.
The Digital Fraud Risk Control on the Electronic-based Companies

Cybercrime has become the main concern of an agency or business nowadays. It says that 71% of organizational threats occur because of the failure of the company to monitor the software, according to the Global Risk Management Report. Although, 74% is disputed because the IT system is not being updated. This research focuses on Indonesia, and according to Patroli Siber Web site, there are 815 cases of offensive material that have been disseminated, which are followed by web fraud in as many as 530 cases. And also focused on unauthorized access in 104 cases, data theft in 36 cases, device reduction in 17 cases, and data manipulation in as many as 54 cases. For 1361 instances, WhatsApp social media was ranked first in social media, followed by Instagram 1288 instances, Facebook 596 instances, and email 108 instances. This research is also carried out to provide input to non-bank organizations to take the future preventive steps against the risk of cybercrime. The outcome found that for non-bank organizations, certain steps need to be taken in risk reduction.
An Enhanced Harmony Search Based on Quantum Mechanism

Harmony search (HS), which is widely used in many fields, is a meta-heuristic algorithm. In each iteration, HS generates a new solution under the harmony search consideration rate (HMCR) from harmony memory (HM). And the new solution is fine-tuned through a pitch adjustment rate (PAR). The basic HS obtains a random value from HM for each variable of a solution, and adjusts it in a fixed range. For the sake of improving its performance, we present an enhanced harmony search based on quantum mechanism (QEHS), which incorporates quantum concepts and differential mutation operation into the harmony search algorithm. QEHS reinforces the exploration and exploitation capability in search space, with the use of wave function from Schrödinger formula to express the harmony in HM. For reflecting the effectiveness of QEHS, simulations are carried on 30 benchmark functions from CEC2014. To manifest its feasibility, we compared the results of QEHS with that of other HS variants and some popular algorithms. Facts turn out that QEHS is an efficient and competitive algorithm.
Enhancement of light field disparity maps by reducing the silhouette effect and plane noise

Abstract During the last decade, there has been an increasing number of applications dealing with multidimensional visual information, either for 3D object representation or feature extraction purposes. In this context, recent advances in light field technology, have been driving research efforts in disparity estimation methods. Among the existing ones, those based on the structure tensor have emerged as very promising to estimate disparity maps from Epipolar Plane Images. However, this approach is known to have two intrinsic limitations: (i) silhouette enlargement and (ii) irregularity of surface normal maps as computed from the estimated disparity. To address these problems, this work proposes a new method for improving disparity maps obtained from the structure-tensor approach by enhancing the silhouette and reducing the noise of planar surfaces in light fields. An edge-based approach is initially used for silhouette improvement through refinement of the estimated disparity values around object edges. Then, a plane detection algorithm, based on a seed growth strategy, is used to estimate planar regions, which in turn are used to guide correction of erroneous disparity values detected in object boundaries. The proposed algorithm shows an average improvement of 98.3% in terms of median angle error for plane surfaces, when compared to regular structure-tensor-based methods, outperforming state-of-the-art methods. The proposed framework also presents very competitive results, in terms of mean square error between disparity maps and their ground truth, when compared with their counterparts.
A Privacy-Preserving Access Mechanism of Engineer Station in Distributed Control System

Controller configuration program in the engineer station is an important part of distributed control system (DCS). In order to prevent the configuration program from being tampered with maliciously, we propose a privacy-preserving access mechanism of engineer station for DCS in this paper. In this scheme, we combine a publicly verifiable secret sharing (PVSS) and a symmetric encryption technology to protect the controller configuration program. Security analysis shows that the proposed scheme can realize the secure access mechanism of engineer station in DCS, which can not only prevent malicious engineer from operating on the engineer station alone, but also verify the legitimacy of the shares submitted by the participating engineers. Therefore, our scheme guarantees the security of the controller configuration program and can better meet the requirements of DCS.
Text Summarization using Formal Argumentation

Current approaches to text summarization are not genuinely interested in how competent readers perform the task often by re-constructing the arguments in the text then arriving at the summary from conclusions of acceptable arguments. This paper aims to mimic this natural path using formal argumentation techniques. Assuming the availability Argumentative Discourse Unit (ADU) graph of the given text, we build structured argumentation frameworks called S-ASPIC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">+</sup> and ABA representing the text. Then we use ABA proof procedures to re-construct arguments in the text and evaluate their acceptabilities. Finally, we aggregate the conclusions of acceptable arguments. We demonstrate our approach using a dataset of argumentative micro-texts and report the results, describing comparisons to other methods.
Towards Indonesian Phrase Extraction: Framework and Corpus

Mining quality phrases is one of the basic tasks of natural language processing. Current research mainly focuses on universal languages but is rarely conducted for low-resource languages such as Indonesian. To the best of our knowledge, there is no evaluation dataset available for phrase extraction task in Indonesian. Phrase extraction is a challenging task for Indonesian due to the lack of language analyzing tools and large data set. Therefore, we propose a framework to construct Indonesian phrase extraction corpus using Wikipedia as high-quality resource and match extracted phrases with our POS-tagged corpus. Our linguistic experts manually classified the extracted POS patterns. With the annotated patterns, we re-extract phrases and construct a corpus with 8379 Indonesian phrases in total. In addition, we experiment with three deep learning models achieved superior performances for phrase extraction and finalize the baselines for Indonesian phrase extraction task.
“ENTERTAINING” THEORY OF RELATIVITY IN THE LESSONS OF RFL: VIEWING THE FEATURE FILM “INTERSTELLAR”

Nowadays the number of foreign students entering various educational programs at Russian universities is actively growing. Multimedia technologies are actively used for their training along with traditional forms of work. This paper discusses the features of working with video material in physics lessons in a foreign language classroom. Such classes help to increase the motivation of students to acquire knowledge and successfully master the main course material. The authors focus their attention on a visual demonstration of the basic provisions of the general theory of relativity, for which the Oscar-winning film “Interstellar” by Christopher Nolan is chosen. The paper describes the main stages of the lesson such as preparatory, viewing and final, as well as their main aims. The authors offer different types of tasks and exercises that prepare students for watching a scientific and feature film on the topic under study, control their understanding and learning of theoretical information. The paper provides examples of the implementation of all types of speech activity while working on the selected topic. The authors demonstrate that this form of the lesson allows one to form not only communicative competence, such as the ability to answer questions and present the studied material, but also professional competence, such as the ability to solve tasks, formulate hypotheses, and scientifically argue one’s point of view. The proposed system of organizing the lesson proves the importance and efficiency of including video material in the process of studying physics. It is necessary to emphasize the practicality of the work that offers a well-planned lesson, which can be adapted to the level of a student (either a student of the pre-academic programs, bachelor’s or master’s). In addition, the presented types of tasks can form the basis for creating a similar lesson on any other topic.
Wavelet Packets and Frame Packets

We have observed in the previous chapters that using a prime element of a local field K of positive characteristic, the concepts of multiresolution analysis (MRA) and wavelet can be generalized to such a field. In this chapter, we prove a version of the splitting lemma for this setting and using this lemma we construct the wavelet packets associated with such MRAs. We show that these wavelet packets generate an orthonormal basis for \(L^2(K)\) by translations only. We also prove an analogue of splitting lemma for frames and construct the wavelet frame packets in this setting. Analogous to the orthonormal case, we define the concept of biorthogonal wavelet packets and show that they provide Riesz bases as long as the splitting procedure is applied finitely many times. We also find necessary conditions when we assume that the biorthogonal packets are uniformly bounded.
Background Manipulation with Computer Vision

A Background gives an extra advantage to one’s presentation. In today’s world, we are digitalizing every section of the professional lives of humans, and often we need to be present digitally in an event. In those times, we look for a place which is has a decent background where we can present ourselves while maintaining our privacy. Privacy is important to everyone and we try to ensure our private details are not made public in any way. However, it is not possible for everyone to ensure a common background. If we can select our background digitally then it would solve this issue. Computer vision can help us in manipulation of background during video calls and ensure safety of our personal information. For this purpose, we are going to use Open CV to change the background to a video of our choice. We can select a video of our choice and upload it to the module and get the desired background by replacing it. The proposed technique is more efficient and execution time is lesser compared to existing techniques.
Recommendation System: Overview, Current Applications and Future Scope

The purpose of this research paper is to discuss areas of research, current trends and future scope of RecommendationSystem. This paper talks about the functioning of Recommendation Systems, recent trends in IT involving useof RS, approach to build an RS, scope of RS in future and issues associated with it. RS has become a widespreadtool in IT, used by leading Software companies. It has come out as a major subject in IT in the area of research;Over thousand papers have been published on RS and has resulted in acquiring some interesting findings whichwere later got implemented. This paper not only talks about the current applications but also provide a visiontowards its future scope. This research has included different areas where recommendation systems are currentlyin use.
Genesis of Understanding Internet Texts by Ukrainian Adolescents Depending on Their Internet Using Experience

The article presents the results of studying the genesis of the Internet text understanding by young readers depending on the level of their Internet user experience. The investigation reveals the regularities of the dynamics of successful understanding with the growth of the level of the Internet user experience at each stage of the comprehension process. For this purpose, the following methods were used: theoretical (deduction, induction, analysis, synthesis, generalization, systematization), empirical (experiment, method of semantic and pragmatic interpretations, content analysis, subjective scaling), and statistical (primary statistics, correlation analysis). The sample of the experiment consisted of 122 Ukrainian undergraduate students, including 14.8% men and 85.2% women. The study was conducted according to the author's methods "Success of understanding the texts of the Internet" and "Experience and focus of the Internet user". The understanding of Internet texts by adolescents is characterized by the activity of reception (indicating their significant interest in cyberspace), superficial assessment of the text complexity, and the tendency to predict the content by title and illustration. Meanwhile, it represents that the quality of rational and emotional text interpretation is quite low. Young people interpret only a quarter of the content of the message since their emotional understanding is usually inconsistent. However, a rational assessment is more accurate than an emotional one at this age. It is concluded that adolescents' genesis of Internet text understanding depends on their Internet user experience. Under the influence of the Internet user experience, the activity of the reception, the accuracy of expectations according to the illustration, and the consistency of emotional attitudes increase. However, the accumulation of experience sometimes has a negative effect on the dynamics of understanding, in particular on the interpretation of Internet news texts that seem unreasonably simple to young users. It is also recorded that at the stage of emotional identification, the rational evaluation of Internet texts with the accumulation of experience deteriorates. This feature is determined by the effect of unjustified confidence of Internet users.
Improving Video Quality by Predicting Inter-Frame Residuals Based on an Additive 3d-Cnn Model

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A multi-chaotic image encryption algorithm based on bit shuffling and DNA coding

In order to improve the image encryption effect, this paper proposes a multi-chaotic image encryption algorithm based on bit shuffling and DNA coding. First, the hash value of the plain image used to disturb the initial key is obtained through the SHA-256 algorithm. Chaotic sequences are generated by the Lorenz system and the Chen system. Then the shuffle index table used to shuffle the bits of each binary pixel is filled. After that, the eight-bit binary matrix is split into high-four-bit and low-four-bit matrices, and one of the chaotic sequences is rearranged into a matrix with the same size as the plain image, and the index matrices obtained by sorting by row and column are used to scramble the positions of the high and low four-bit matrices. Finally, DNA block coding and operations are performed, and the cipher image is obtained by decoding. The simulation experiment conducted algorithm security tests from the histogram, information entropy, correlation analysis, differential attack and other aspects. The results show that this algorithm can effectively resist common attack methods such as enumeration attacks and differential attacks, and has good security.
Mhnet: Multi-Channel Concating Feature Pyramid Network with Hybrid Attention Mechanism

Object detection is one of the basic tasks in the field of computer vision. In terms of structure, the object detection model based on convolutional neural network still has several space for improvement, such as insufficient feature extraction and large differences for objects of different sizes. In this paper, it proposes an object detection network based on Multi-channel Concating Feature Pyramid with Hybrid Attention Mechanism. Firstly, a new neck, the extended FPN is introdued, which adds some fusion branches. Secondly, a hybrid attention mechanism is designed. And it combines channel attention mechanism with space attention mechanism to strengthen the important feature information. The network can better extract the semantic information and spatial information of the feature map, and improve the classification and positioning capabilities of the network. The experimental results show that the proposed method has excellent performance compared to other popular models on public datasets.