Reviewerly/exHarmony huggingface dataset:
The Construction of a Diabetes-oriented Frequently Asked Question Corpus for Automated Question-Answering Services

In recent years, the prevalence of diabetes has been increasing rapidly worldwide. With the advancement of information technology, automated question-answering services for healthcare, which are commonly based on annotated corpus in health domain, have positive effects on health knowledge spread and daily health management for high-risk populations. This paper proposes to construct a large scale diabetes corpus of frequently-asked questions for automated question-answering services and evaluations. Concentrating on the characteristics of diabetes-related factors that reflect conditions of diabetes, this work establishes an annotated dataset containing professional question & answer pairs about diabetes and their annotated question target categories. The corpus is applicable for various question-answering applications, supporting users to retrieve needed information, arrange diets, adhere to scientific medication as well as prevent and control disease complications.
Continuous Compliance model for Hybrid Multi-Cloud through Self-Service Orchestrator

Cloud Computing offers instant Infrastructure for the needs of Application & Development teams, software development phase accelerated with the adaption of DevOps model. DevOps model key tenets of success are Self-Service, Permission to act, Guardrails and Trust. Since the current Cloud Computing trend is moving towards Hybrid Multi-Cloud the Multi-Tenant phenomenon as well geared up to meet the agile business needs. The Security team's strategy for DevOps, especially related to its Self-Service tenet needs an immediate review. According to the survey in 2019, around 90% of enterprises use some type of Cloud Service; around 50% already adapted Hybrid Multi-Cloud; still 67% security teams had lack of visibility into their cloud infrastructure, security and compliance. Attacks due to Misconfigured Cloud environments was the main cause of data theft Security Incidents. Popular Incidents related to Amazon about Facebook accounts leak and Microsoft about data theft related to Box accounts primary cause is misconfiguration of the concern parties. Hence, Post-Deployment Compliance checks does not suffice the needs of required Security for Hybrid Multi-Cloud environments. Continuous and Pre-Deployment Compliant Self-Service solution for Hybrid Multi-Cloud with appropriate design and implementation procedure is the objective of this paper.
WITHDRAWN: Image integrity verification using blockchain

This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
Experience of using distance technology to teach Chinese undergraduates during the COVID-19 pandemic

The COVID-19 pandemic has significantly affected the organization of the educational process in Russian universities. The object of the research is the experience of implementing e-education and the transition to distance technologies for continuing education of Chinese undergraduates in the Russian metropolitan university. Russian universities had the technical capabilities and used various electronic educational platforms. In the same time, the results of the survey showed a decrease in the satisfaction of Chinese full-time students when switching to distance learning. The study shows the significant factors that had a negative impact on the educational process using distance technology to teach Chinese undergraduates during the COVID-19 pandemic. The authors have grouped these factors into five groups (technical, language, emotional, and financial, content of lecture). Identification of these factors made it possible to develop a program to improve the educational process and enhance the satisfaction of foreign students studying at Russian universities.
Quality management of higher education in the context of digitalization

This paper discusses the main scientific results of the study associated with the introduction of a new understanding of higher education quality management in pedagogical science related to the digitalization of the educational process at the university. The point is that when a student interacts with other subjects of the educational process, conditions are provided in which the student can realize his or her importance as a subject of the educational process, as a person who models the process of knowing his or her future professional activity, as a future member of the professional community. This allows the student to see the paths of his or her development, to identify key areas in self-organization of activities, in self-development of individual characteristics and personality traits. The implementation of the manage-help scheme, coming to replace the influencing scheme, ensures the participation of the student in building an individual learning path, in the choice of customized forms, methods and technologies of professional training in accordance with individual capabilities, needs and abilities. The research result is the development and scientific justification of the digital quality management system of higher education in the interactive intellectual environment developed by the author. The developed system allows the student to move away from the existing templates and stamps of professional activity, expands his or her opportunities in development (and self-development) and creativity at university.
Multi-agent Cooperative Search based on Reinforcement Learning

Regional cooperative search is an important scenario for Unmanned Aerial Vehicle (UAV) fleet. It is difficult for the UAV fleet to adapt to the dynamic environment by using traditional search method, which needs pre-planning. In this paper, a reinforcement learning approach is employed to train the UAV agents to search without pre-determined strategies. By designing reward function, state and action spaces, agents can learn to make decisions all by themselves based on their own observations and cooperate with each other effectively. The proposed method is experimentally validated by numerical simulations, and the result show that the UAV fleet can complete task with a pretty high coverage and few repetitions at the same location.
The technique for future teachers’ digital literacy development

Abstract The relevance of the issue is related to the fact that the level of teachers’ digital literacy who transmit their knowledge, skills, and attitudes to the new generation depends on the further development of the Russian digital economy. The social order of society leads to the need for training teachers with high-level digital literacy. This article proposes a technique for increasing the level of future teachers’ digital literacy. The following general scientific methods were used to solve the research problems: analysis, synthesis, generalization, and analogy. A system approach, allowing to consider the relationship and interdependence of all studied elements is important, especially, such a variety as structural-functional analysis, which can represent the phenomena structure under consideration in the dynamic and static slice. Empirical methods such as questionnaires, measurement, comparison, and description are of great importance for the work. The article presents the formation and digital literacy development as a system and a controlled process. A technique for improving the level of future teachers’ digital literacy, based on the practical development of interactive online platforms, is proposed, and its effectiveness is experimentally confirmed. The evaluation of this technique’s effectiveness for final year bachelor students was carried out.
TDL-Chain: An Intelligent Data Transmission Control System in Tactical Data Link Based on Blockchain

Tactical data link (TDL) is a wireless communication system that achieves instant, accurate, automatic, and secure data transmission in military scenarios. It greatly reduces the time of Observation, Orientation, Decision, and Action (OODA). Modern TDL systems utilize a distributed architecture to enhance anti-destruction capacity and support data transmission among different TDLs. However, it is difficult to ensure data consistency among TDLs, which greatly reduces joint combat capacities of TDLs. A data transmission control system that can offer data consistency among various TDLs is highly expected. Besides, data transmission in TDLs should be controlled in an automatic and intelligent way to satisfy the low latency requirement of modern wars. To tackle this problem, in this paper, we employ blockchain-based smart contracts to build up a decentralized data transmission control system for TDLs called TDL-Chain. TDL-Chain achieves automatic data transmission and guarantees information consistency among various combat entities. The evaluation based on proof-of-concept implementation demonstrates the effectiveness and efficiency of TDL-Chain.
Online Shop Recommendations: Decision Support System Based on Multi-Objective Optimization on the Basis of Ratio Analysis

Business technological advancement facilitates human activities through online shopping. The number of online shops in the marketplace offers many kinds of products with attractive marketing strategies; thus, the customers are confused with the product comprising. Therefore, this research tries to provide an optimal online shop recommendation as an alternative solution. The Decision Support System (DSS) approach on management model applied Multi-Objective Optimization on the Base of Ratio Analysis (MOORA) for the analytical calculation by considering several criteria, including price, rating, discount, a product sold, and response chat. It reveals the ranking of fifty online shops in the marketplace as the maximum alternatives' product recommendations. Thus, the customers will be smartly guided to choose the high-quality product at the greatness services of an online shop. The mechanism of DSS based on MOORA was applied through the construction of a prototype system, namely DSS-MyProduct. DSS-MyProduct suggests the buyers with optimal products and the greatest online shop choice for shopping. The application has been tested by using Blackbox and User Acceptance Test (UAT) testing, which indicated that the application could perform the functions and operational procedures appropriately. 83.4% of users agreed that this DSS-MyProduct aids them in deciding on the optimal choice preferred in shopping. The comparison of user manual selection and system calculation shows a positive outcome on the accuracy of the system. Hence, this application can be used by the marketplace as a smart recommendation tool for product selection.
3-Party Adversarial Steganography

Steganography enables a user to hide information by embedding secret messages within other non-secret texts or pictures. Recently, research along this direction has picked a new momentum when Hayes & Danezis (NIPS 2017) used adversarial learning to generate steganographic images. In adversarial learning, two neural networks are trained to learn to communicate securely in the presence of eavesdroppers (a third neural network). Hayes–Danezis forwarded this idea to steganography where two neural networks (Bob & Charlie) learn “embed” and “extract” algorithms by exchanging images with hidden text in presence of an eavesdropping neural network (Eve). Due to non-convexity of the models in the training scheme, two different machines may not learn the same embedding and extraction model even if they train on the same set of images. We take a different approach to address this issue of “robustness” in the “decryption” process. In this paper, we introduce a third neural network (Alice) who initiates the process of learning with two neural networks (Bob & Charlie). We implement and demonstrate through experiments that it is possible for Bob & Charlie to learn the same embedding and extraction model by using a new loss function and training process.
Feature Engineering for Various Data Types in Data Science

The precision of any machine learning algorithm depends on the data set, its suitability, and its volume. Therefore, data and its characteristics have currently become the predominant components of any predictive or precision-based domain like machine learning. Feature engineering refers to the process of changing and preparing this input data so that it is ready for training machine learning models. Several features such as categorical, numerical, mixed, date, and time are to be considered for feature extraction in feature engineering. Datasets containing characteristics such as cardinality, missing data, and rare labels for categorical features, distribution, outliers, and magnitude are currently considered as features. This chapter discusses various data types and their techniques for applying to feature engineering. This chapter also focuses on the implementation of various data techniques for feature extraction.
Accuracy of bone segmentation and surface generation strategies analyzed by using synthetic CT volumes

Different kinds of bone measurements are commonly derived from computed-tomography (CT) volumes to answer a multitude of questions in biology and related fields. The underlying steps of bone segmentation and, optionally, polygon surface generation are crucial to keep the measurement error small. In this study, the performance of different, easily accessible segmentation techniques (global thresholding, automatic local thresholding, weighted random walk, neural network, and watershed) and surface generation approaches (different algorithms combined with varying degrees of simplification) was analyzed and recommendations for minimizing inaccuracies were derived. The different approaches were applied to synthetic CT volumes for which the correct segmentation and surface geometry were known. The most accurate segmentations of the synthetic volumes were achieved by setting a case-specific window to the gray value histogram and subsequently applying automatic local thresholding with appropriately chosen thresholding method and radius. Surfaces generated by the Amira® module Generate Lego Surface in combination with careful surface simplification were the most accurate. Surfaces with sub-voxel accuracy were obtained even for synthetic CT volumes with low contrast-to-noise ratios. Segmentation trials with real CT volumes supported the findings. Very accurate segmentations and surfaces can be derived from CT volumes by using readily accessible software packages. The presented results and derived recommendations will help to reduce the measurement error in future studies. Furthermore, the demonstrated strategies for assessing segmentation and surface qualities can be adopted to quantify the performance of new segmentation approaches in future studies.
An Efficient Implementation of AES on 8-Bit AVR-Based Sensor Nodes

Recently, FACE-LIGHT was proposed on 8-bit AVR MCUs for fast AES encryption. FACE-LIGHT is an extended version of Fast AES-CTR mode Encryption (FACE) method which was firstly proposed for high-end processors and it is tailored for performance on 8-bit AVR MCUs. Even though it has achieved high performance, it has to suffer from the overhead caused by table generation. Thus, when the number of blocks is less than a certain number, the table generation overhead is greater than the gains from using the generated table in the process of encryption. In other words, FACE-LIGHT needs to generate new tables whenever the Initial Vector (IV) is changed. Thus, frequent table regeneration results in a significant performance degradation. In this paper, we present an efficient implementation of AES block cipher on 8-bit AVR Microcontrollers (MCUs). Our method combines ShiftRows, SubBytes, and MixColumns operations into one with column-wise fashion and makes full use of registers of AVR MCUs for high performance. With handcrafted assembly codes, our implementation has achieved 2,251, 2,706, and 3,160 clock cycles for 128-bit, 192-bit, and 256-bit security, respectively. Our implementation outperforms FACE-LIGHT with respect to overall performance including table generation and block encryptions until around 1,850 blocks (resp. 15,000 blocks) for 128-bit (resp. 192-bit) security. With respect to 256-bit security, our implementation always outperforms FACE-LIGHT without considering the table generation time. Our implementation operates in constant time and can be used for not only CTR mode, but also CBC mode differently from FACE-LIGHT.
Reference guided image super-resolution via efficient dense warping and adaptive fusion

Due to the limited improvement of single-image based super-resolution (SR) methods in recent years, the reference based image SR (RefSR) methods, which super-resolve the low-resolution (LR) input with the guidance of similar high-resolution (HR) reference images are emerging. There are two main challenges in RefSR, i.e. reference image warping and exploring the guidance information from the warped references. For reference warping, we propose an efficient dense warping method to deal with large displacements, which is much faster than traditional patch (or texture) matching strategy. For the SR process, since different reference images complement each other, and have different similarities with the LR image, we further propose a similarity based feature fusion strategy to take advantage of the most similar reference regions. The SR process is realized by an encoder–decoder network and trained with pixel-level reconstruction loss, degradation loss and feature-level perceptual loss. Extensive experiments on three benchmark datasets demonstrate that the proposed method outperforms state-of-the-art SR methods in both subjective and objective measurements.
Secure Distributed Network Model to Store Vehicle Transaction Records Through Blockchain Platform

Blockchain is an emergent technology applied initially to cryptocurrencies, but with a much further reach. We present a secure, transaction-based system that handles motorized vehicle provenance (including ownership) over a distributed model using smart contracts on a blockchain. The system model includes the specification of various security protocols, verified using AVISPA-SPAN tool and guaranteeing privacy, authenticity, and other security features. A client of the system can have different roles accordingly to the relationship it has with the vehicle, and it can get various services and request transactions accordingly. Our proposal might contribute to reduce fraud and expand the applications of Blockchain.
Classification of Falciparum Parasite in Human Red Blood Cells Using Randomly Wired Neural Network

Malaria is one of the diseases which is caused by the Parasite Plasmodium, such as Falciparum. This parasite is infected into red blood cells of humans by mosquitoes. The infection can be detected using taking a sample of the red blood cells and testing under the microscope. However, testing can be conducted only by experts, and it needs much time to complete. In this study, we will implement a Randomly Wired Neural Network to detect the parasite falciparum in red blood cells. We have been used 27,558 images of red blood cells as data, which were colored by Giemsa. The result of 5-fold cross-validation using the Randomly Wired Neural Network with four random graphs is better than the method of transfer learning in an average accuracy of 95.08% and 91.99%, respectively.
Digitalization and reasoning over engineering textual data stored in spreadsheet tables

Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence requires industry to find ways of extracting the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make explicit the classes and relations held in spreadsheet tables. Ontologies offer a pathway, as they are used to define machine-interpretable definitions of shared concepts, and relations between concepts, using formal descriptions. We illustrate this with a case study on a Failure Modes and Effects Analysis (FMEA) table, using an example from the IEC 60812 FMEA Standard. Our example demonstrates how the relationship between rows and columns in a table can be represented in logic. Further, we give relationships in the FMEA and asset hierarchy spreadsheets an explicit representation, so that OWL-DL reasoning can infer final failure effects at the system level from component failures. The prototype ontologies described in this paper are modular and aligned to a Top Level Ontology, and hence can be applied to other use cases. Our contribution is to show engineers needing to make data captured in spreadsheet tables machine readable, how ontologies can be applied using a real example.
Research on the Application of Transparent Encryption in Distributed File System HDFS

More and more companies are turning to Hadoop to store and deal with their most valuable data, the protection and management of data begin to face increasing security risks. As a data protection technology closely related to big data platform, HDFS transparent encryption technology has certain practical value. In this paper, the HDFS transparent encryption technology is studied, the implementation principle of HDFS transparent encryption technology is introduced, the security and performance of the HDFS transparent encryption technology are analyzed, and the encryption technologies at different levels of the big data platform are compared. Research shows that HDFS transparent encryption technology has the advantages of high performance, transparent application and easy deployment, but there are still potential security problems in the application process.
Bidirectional Temporal-Recurrent Propagation Networks for Video Super-Resolution

Recently, convolutional neural networks have made a remarkable performance for video super-resolution. However, how to exploit the spatial and temporal information of video efficiently and effectively remains challenging. In this work, we design a bidirectional temporal-recurrent propagation unit. The bidirectional temporal-recurrent propagation unit makes it possible to flow temporal information in an RNN-like manner from frame to frame, which avoids complex motion estimation modeling and motion compensation. To better fuse the information of the two temporal-recurrent propagation units, we use channel attention mechanisms. Additionally, we recommend a progressive up-sampling method instead of one-step up-sampling. We find that progressive up-sampling gets better experimental results than one-stage up-sampling. Extensive experiments show that our algorithm outperforms several recent state-of-the-art video super-resolution (VSR) methods with a smaller model size.