Reviewerly/exHarmony huggingface dataset:
Mass Sphering Approach for Three-Dimensional Reconstruction of Brain Tumor using MRI

Objective: The objective of the research work is to reconstruct the brain tumor three-dimensionally with high degree of accuracy.Methods: This study describes 3D reconstruction of brain tumor using Mass Sphering Approach (MSA) algorithm.39 weighted features are extracted from the non-tumor and tumor pixels.These weighted features are used to train the Support Vector Machine (SVM) algorithm.Number of training samples taken to train SVM algorithm are 268 and the testing sample are 64.The complete MR image set of a subject (64 axial slices) are detected for tumor pixels and these slices are concatenated to get volumetric tumor information.Findings: 5-step MSA algorithm is proposed which includes filtering, segmentation, classification, optimization and reconstruction.MR images are subjected to Rician noise which can be removed by a simple correction scheme, initiated to change the bias due to the Rician distribution of the noisy magnitude data.The filtered MR image slices are segmented and classified to detect the tumor areas and the tumor pixels are subjected for 3D reconstruction.The improvement in performance of MSA is depicted by comparing the algorithm with traditional SVM.Novelty: The accuracy achieved in detecting glioma and glioblastoma using MSA are 95.24% and 99% respectively which is highly remarkable.
Perkembangan Kesenian Dzikir Saman di Desa Wanagiri Kecamatan Saketi Kabupaten Pandeglang Tahun 1998-2017

The purpose of this study is: (1) describing the history of the formation of Dzikir Saman art in Wanagiri Village, Saketi District of Pandeglang Regency, (2) describing the development of Dzikir Saman art in Wanagiri Village, Saketi District, Pandeglang Regency in 1998-2017, (3) describing community efforts in preserving Dzikir Saman art as intangible cultural heritage in Pandeglang. The study uses historical methods consisting of heuristics, criticism, interpretation and historiography. The theoretical approaches used are cultural theory and challenge theory and response (response) from Arnold Toynbee. The study took year limits between 1998-2017. In 1998 as the beginning of the development of Dzikir Saman art in Wanagiri Village and in 2017 Dzikir Saman art in Wanagiri Village was named as Intangible Cultural Heritage by the Ministry of Education. The results of the study revealed that the art of Dzikir Saman in Wanagiri Village began from a teaching of the sammaniyah order. This sammaniyah order is a well-known teaching in Sufi science brought by Syech Samman in the 18th century. Then in the development of Dzikir Saman art in Wanagiri Village experienced the addition of function, which initially this art was only presented in religious ceremonies only on the anniversary of the Prophet Muhammad's Maulid then saw the rapid development of the times, it became a challenge for the existence of Dzikir Saman art, finally the art of Dzikir Saman was presented also in the form of performing arts, Then in its development also the art of Dzikir Saman Wanagiri Village was named as Intangible Cultural Heritage in 2017 by the Ministry of Education. Furthermore, there are preservation efforts carried out by Dzikir Saman art players, Pandeglang community and there is government carrying capacity, so that the existence of Dzikir Saman art continues to grow, these efforts include: (1) Dzikir Saman Wanagiri Village players do training every week even though this art is not staged, (2) pandeglang community always presents Dzikir Saman art in commemoration of Prophet Muhammad's Maulid, Then there is the study of Dzikir Saman art by researchers which is one form of preservation in the field of education, (3) the existence of government carrying capacity is by presenting Dzikir Saman art in a series of Pandeglang cultural festival events.
Recent development on intelligent computing

The articles appearing in this special issue on Recent Development on Intelligent Computing are extended versions of the papers presented at the 2020 Sixteenth International Conference on Intelligent Computing (ICIC2020) held on October 2–5, 2020, in Bari, Italy. All the papers included here have been thoroughly reviewed and revised with the support of many reviewers under the Elsevier Editorial System (EES). 19 papers representing less than five percent of all eligible papers accepted at the ICIC2020 are selected for inclusion in this special issue. The selected papers are organized into the following three sections.
Analyzing the content of the websites of RAS Siberian Branch research institutions

The analysis of websites/web-pages of content of SB RAS research institutions is based on the content assessment checklist developed specifically for this study. The checklist comprised 50 questions divided in 5 sections: website information, web-page design and functions, content and web-services. The websites of Novosibirsk, Buryat, Irkutsks, Kemerovo, Krasnoyarsk, Omsk, Tomsk, Yakutsk and Tyumen research centers were selected for the study, 44 library websites altogether. The online survey was conducted within the study comprising two questions: Who manage and maintain the website/web-page of your library? Is this library website management strategy efficient? The respondents were also asked to propose on how to improve mangement of the institutional website. The web-content assessment enables to reveal problems and trends which would be useful for re-designing the library websites of research institutions. The author offers recommendations based on the study findings.
Faster R-CNN based Traffic Sign Detection and Classification

Traffic sign is the key aspect in road and also for the autonomous car. Detection and classification of these sign plays a vital role for the invention of driverless vehicles. Convolutional neural network (CNN) has the ability to learn local features using series of convolutional and pooling layer observing the image sequences. In this work, traffic sign detection and classification has been performed based on deep learning approach. The experiment conducted on Germen Traffic Sign Detection Benchmark (GTSDB) and Recognition Benchmark (GTSRB) for detection and recognition. For traffic sign detection a two-stage detector, Faster R-CNN with ResNet 50 backbone structure is used where the CNN layers extracted the features of traffic signs from the images and the region proposal network (RPN) filter the object from the image to create bounding box based on the extracted feature map. The classification network classifies the traffic signs and predict the proposal confidence score. A general deep learning model is transferred into a specific output with weights with transfer learning by tuning the pretrained model based on COCO image dataset. The performance is compared with ResNet 152, MobileNet v3 and RetinaNet based on the confidence score and mean average precision (mAP). Faster R-CNN with ResNet-50 shows better detection performance comparing with other backbone structure. In addition, a series of convolution layer with batch normalization followed by max pooling layer is used to build a classifier and softmax is used in the output for 43 class classification and 97.89% test accuracy has been obtained.
Network Representation

Abstract Network representation learning aims to embed the vertexes in a network into low-dimensional dense representations, in which similar vertices in the network should have “close” representations (usually measured by cosine similarity or Euclidean distance of their representations). The representations can be used as the feature of vertices and applied to many network study tasks. In this chapter, we will introduce network representation learning algorithms in the past decade. Then we will talk about their extensions when applied to various real-world networks. Finally, we will introduce some common evaluation tasks of network representation learning and relevant datasets.
Práticas integrativas e complementares utilizadas para manejo da dor em idosos: revisão integrativa

The aim was to identify alternative and complementary practices used in the elderly for pain management in the literature. This is an integrative literature review. Five studies published from 2017 to 2020 were included. As a result, alternative interventions applied to the elderly for pain management were: acupuncture, lian gong, shiatsu, massage, reiki and chiropractic care. Studies have shown significant pain relief after the interventions. The population had a chronic health condition, a predominance of females and the mean age ranged from 50 years to 71.6 years. It is concluded that research indicates few publications on the use of alternative therapies, given the variety provided and these interventions reflect positively on the pain management of the elderly.
Semantic context driven language descriptions of videos using deep neural network

Abstract The massive addition of data to the internet in text, images, and videos made computer vision-based tasks challenging in the big data domain. Recent exploration of video data and progress in visual information captioning has been an arduous task in computer vision. Visual captioning is attributable to integrating visual information with natural language descriptions. This paper proposes an encoder-decoder framework with a 2D-Convolutional Neural Network (CNN) model and layered Long Short Term Memory (LSTM) as the encoder and an LSTM model integrated with an attention mechanism working as the decoder with a hybrid loss function. Visual feature vectors extracted from the video frames using a 2D-CNN model capture spatial features. Specifically, the visual feature vectors are fed into the layered LSTM to capture the temporal information. The attention mechanism enables the decoder to perceive and focus on relevant objects and correlate the visual context and language content for producing semantically correct captions. The visual features and GloVe word embeddings are input into the decoder to generate natural semantic descriptions for the videos. The performance of the proposed framework is evaluated on the video captioning benchmark dataset Microsoft Video Description (MSVD) using various well-known evaluation metrics. The experimental findings indicate that the suggested framework outperforms state-of-the-art techniques. Compared to the state-of-the-art research methods, the proposed model significantly increased all measures, B@1, B@2, B@3, B@4, METEOR, and CIDEr, with the score of 78.4, 64.8, 54.2, and 43.7, 32.3, and 70.7, respectively. The progression in all scores indicates a more excellent grasp of the context of the inputs, which results in more accurate caption prediction.
The Library of General Staff and Main Headquarters (All-Russia Main Headquarters) in 1908–1919

The history of the first military library from 1908 (after its innovation after 1900 fire), and up to its relocation to the Main Headquarters’ Academy in 1919, is explored. The study is based on the archival documents being introduced into scientific discourse. The focus is made on: the Library development in 1908– 1911; interaction with Imperial Russian Military Historical Society; Library during the WWI; relocation to the RSFSR’s new capital and its settlement in Moscow. Periodicals and unpublished materials in the Russian State Military Archive made the main sources of the article. The main directions of the Library activities within the mentioned period, considerations and problems of Petrograd-to-Moscow relocation, are discussed. The whole way of the book collection relocation in 1918– 1919 is revealed and explored. The state of the library collection remaining in Petrograd is characterized. The article contributes to the knowledge of the history of the General Staff and Main HQ Library in the 1920s.
RETRACTED ARTICLE: Encryption Method for Blockchain based Data for Safe Transmission Using Fuzzy Algorithm

In the digital era, everything around us is connected to the internet. Organizations are keeping their valuable data on the cloud and users may access the data/services at any time from any location. This makes user’s life comfortable but it also brings security risks in the current scenario of information sharing on blockchain enabled systems. In order to improve the security of digital social data transmission, an encryption method for the safe transmission of data based on a fuzzy encryption algorithm is proposed. The digital blockchain based data cache is combined with the application layer protocol recognition technology to achieve redundant data filtering. In this research, the fuzzy matching encryption method based on data preprocessing is proposed to encrypt the data. Firstly, the first character in the sequence is sorted and encrypted with the idea of operations, and then the optimization of Hill sequence is performed to encrypt the remaining character set on the basis of fuzzy encryption. The symmetric encryption algorithm and asymmetric encryption algorithms are combined to ensure the safe and efficient transmission of digital social data and the safe distribution of encryption key. At the same time, information authentication technology is introduced to prevent information from being tampered and to realize the safe transmission of data through multiple encryptions. Experimental results show that the proposed method can effectively remove redundant data, and the data has strong anti-attack, high query efficiency in the transmission process, showing good practicality.
The Role of Picture Books in Raising Children's Understanding of English Literature and Life Science Concepts: Selected Stories by Eric Carle

Abstract&#x0D; The current study is a theoretical study that aims to underline the role of picture books as a serious genre of children's literature in raising children's understanding of English literature and life concepts; especially for non-English speakers. Unfortunately, most Iraqi people have developed a social phobia of learning English since childhood. This phobia is resulted from the heavy traditional reading and writing assignments as well as hard exams. Therefore, this study suggests incorporating more interesting literary material like picture books that would bring pleasure and help in raising children's love and cognition of English Language. More significantly, it calls to replace the old curriculum with a more vital one where children can interact with all their senses; visual, auditory, and kinesthetic (VAK). To make this possible, two of Eric Carle's books The Very Hungry Caterpillar and The Tiny Seed have been carefully selected according to the American and English elementary school teachers' standards for children aged 3-6 years old. Each story element was submitted to a literary analysis, including pictures, life concepts, and language that enhance children's understanding of literature. Based on Piaget's view about the importance of involving sensorimotor actions in learning to help in children's cognition development, some VAK lesson plans and activities were designed using the concept development model and Synectics strategy. The study has concluded that incorporating picture books into the school curriculum and sensorimotor activities like coloring, cutting paper, games, sounds, and music would help in raising children's understanding of English literature and life science more interactively.
Mid-State Kalman Filter for Nonlinear Problems

When tracking very long-range targets, wide-band radars capable of measuring targets with high precision at ranges have severe measurement nonlinearities. The existing nonlinear filtering technology, such as the extended Kalman filter and untracked Kalman filter, will have significant consistency problems and loss in tracking accuracy. A novel mid-state Kalman filter is proposed to avoid loss and preserve the filtering consistency. The observed state and its first-order state derivative are selected as the mid-state vector. The update process is transformed into the measurement space to ensure the Gaussian measurement distribution and the linearization of the measurement equation. In order to verify the filter performance in comparison, an iterative formulation of Cramér-Rao Low Bound for the nonlinear system is further derived and given in this paper. Simulation results show that the proposed method has excellent performance of high filtering accuracy and fast convergence by comparing the filter state estimation accuracy and consistency.
“X-Phish: Days of Future Past”: Adaptive &amp; Privacy Preserving Phishing Detection

Website phishing continues to persist as one of the most important security threats of the modern Internet era. A major concern has been that machine learning based approaches, which have been the cornerstones of deployed phishing detection solutions, have not been able to adapt to the evolving nature of the phishing attacks. To create updated machine learning models, the collection of a sufficient corpus of real-time phishing data has always been a challenging problem as most phishing websites are short-lived. In this work, for the first time, we address these important concerns and describe an adaptive phishing detection solution that is able to adapt to changes in phishing attacks. Our solution has two major contributions. First, our solution allows for multiple organizations to collaborate in a privacy preserving manner and generate a robust machine learning model for phishing detection. Second, our solution is designed to be flexible in order to adapt to the novel phishing features introduced by attackers. Our solution not only allows for incorporating novel features into the existing machine learning model, but also can help, to a certain extent, the “unlearning” of existing features that have become obsolete in current phishing attacks. We evaluated our approach on a large real-world data collected over a period of six months. Our results achieve a high true positive rate of 97 %, which is on par with existing state-of-the art centralized solutions. Importantly, our results demonstrate that, a machine learning model can incorporate new features while selectively “unlearning” the older obsolete features.
Android service to interface mosquitto messaging broker (MQTT)

Real-time messages are used at a high level by different computer applications, there are different servers, protocols, etc. that can be used and are available on the Internet, however, these are created in such a way that the consumption of computational resources is very high and would only be optimal if we use a server with great features. This research work aims to develop a service on Android service to interface mosquitto messaging broker (MQTT) and provide a suitable mechanism to receive requests from an Android application at the same time also notify that some data has arrived, data are stored in the SQLite manager, which uses few computational resources and is suitable for small applications, which are testing protocols and messaging servers. The experimental results show that the service meets the initial objectives of the work, allowing to create of a messaging system based on publishers and subscribers for the Android platform.
Third international conference on ecological education “Ecological information and ecological culture”: The review

For over twenty years, the libraries’ contribution to ecological education has been being reflected in the professional program of Crimea World Forum within the section “Ecological information and ecological culture”. In 2018, the section transformed into the conference on ecological education though kept its original name. The Russian National Public Library for Science and Technology (RNPLS&amp;T) and V. I. Vernadsky Non-governmental Ecological Foundation permanently co-organize the Conference. The author reviews the conference events, as well as problems and tasks discussed at the Conference; includes the abstracts of papers delivered (the full texts are accessible via the RNPLS&amp;T’s website). The participants in the Conference agreed that the libraries had to adopt the integrated approach toward the UN Sustainable Development Goals (SDGs) and to see themselves participating in the global movement for stable and sustainable future. For this purpose the libraries possess every resource: the books embrace the best experience of the humankind; the professionals who are able to retrieve these books and to offer them to their patrons, and to orient themselves in information flows. The true goal of the Conference is to promote the SDGs more so as the Russian libraries are traditionally serious about ecological education.
Exploring the Components of Digital literacy Curriculum: EFL and IT Instructors’ Voice

Every day we are moving toward digitalization and computerization. In education and especially online learning, we are observing fast developments. The need of a digital curriculum and digital literacy for better teaching and learning is sensible. Thus, a digital literacy curriculum is a necessary tool for teachers and students. This qualitative study explored some components of the digital literacy curriculum based on EFL and IT instructors’ voice. Also, the researchers found digital tools that they used a lot and their purposes behind applying these digital tools. The results show that EFL and IT instructors had focused on production, communication, evaluation of digital skills in cyberspace. Most of them were aware of digital literacy definition and suggested some components of the digital literacy definition components and suggested designing a digital literacy curriculum. Among all digital tools, they used WhatsApp and Bigbluebutton more. Based on all retrieved data, the researchers proposed a digital literacy curriculum framework and identified the need for further study about applying this framework in real life. Doing need analysis before designing a digital literacy curriculum to know students' and teachers' digital talent and awareness is another area that further research should have done.
FACULTY MEMBERS' DIGITAL FOOTPRINT EXPERIENCES AND DIGITAL FOOTPRINT AWARENESS

This study aimed to reveal the relationships between faculty members' gender and Internet usage time (daily usage time and years of use), their use of online environments, their digital footprint experiences, and digital footprint awareness. For this purpose, the relational research model was adopted in the study. The sample of the study consists of 398 faculty members working at a state university in Turkey. Descriptive and relational analyzes were made for the analysis of the data. Mean, percentage, and frequency analysis were used in descriptive analysis. For the relational analysis, the association rule, one of the data mining methods, was used. At the end of the study, the faculty members' digital footprint awareness was high, and their negative digital environment experiences were low. It was observed that the digital footprint experiences and awareness of female faculty members were significantly higher than that of male faculty members. It was found that faculty members mostly use online chat tools, social networks, e-mail services, and least blogs, learning management systems, and Wiki. Faculty members who indicated that digital media posts reflect their real thoughts stated that they do not regret these posts, and after checking their writing, they are sharing them in digital media. Finally, researchers were suggested to examine the change in digital footprint awareness according to regional and cultural differences.