Reviewerly/exHarmony huggingface dataset:
A Study on University Students’ Perception on International Development Cooperation and Global Citizenship Education

This study was conducted to understand the overall perceptions of university students toward International Development Cooperation (IDC) and global citizenship education. The results, based on survey data, are as follows.&lt;br/&gt;First, university students agreed on the necessity of pursuing the goals of IDC and ODA projects. They reasoned that because South Koreans had benefited from foreign aid, they had a moral responsibility to contribute to the welfare of other developing countries. The students presented a high degree of agreement on the effectiveness of IDC projects in solving global problems, but they showed reluctance to the enlargement of the projects. The study revealed that the students were less aware of “2030 Sustainable Development Goals (SDGs)”&lt;br/&gt;Second, the study showed that undergraduates were willing to participate in IDC projects and global citizenship educations. From a macroscopic point of view, they showed an eagerness to help the poor in developing countries and be engaged in IDC projects due to their global concerns. From a microscopic view, they wanted to fulfill their sense of moral responsibility by participating in global citizenship education, which they believe might help them find a job and develop their career.&lt;br/&gt;Third, undergraduates recognized that universities lack proper education on IDC and need to be equipped with a more enforced educational programs. Some of the students expected to receive multifaceted and up-to-date information on IDC via liberal arts courses.
The project design of closure with cofferdam in a large reclamation project

The closure of the reclamation project generally requires that the sluice has been built and can be normally opened and closed for drainage. There is a large amount of follow-up work after closured of this project. In order to reach the safe flood control standard for the next typhoon season after the closure, reduce the flood control risk, and strive for sufficient time for the subsequent construction of the seawall, it is necessary to closure in advance under the condition that the sluice has not yet been opened. Therefore, this project demonstrated the feasibility of the closure with cofferdam based on the premise of think outside the box and theoretical calculation. The successful completion and acceptance of the reclamation project proved that the project is successful.
Construction of a General Lexical-Semantic Knowledge Graph

One of the main factors that restricts the performance of deep learning models is the lack of exogenous knowledge, which makes the model fail to understand the lexical semantic relationships in real situations. For this reason, building a general lexical-semantic knowledge graph to give the model external knowledge to improve the performance in generalization and robustness of the model has become a hot research topic. This article introduces a method of constructing a general lexical-semantic knowledge graph (GLSKG). GLSKG stores useful knowledge such as definitions, parts of speech, and examples of vocabularies. Besides, it contains detailed knowledge such as lexical semantic relationships and semantic dependency relationships among words. The construction of GLSKG introduced in this paper is carried out in two steps: i) redesign of the architecture of the knowledge graph and ii) integrate existing knowledge graphs. The scale of GLSKG is considerable, including 295,734 entities, 170 semantic dependency relations, and eight lexical-semantic relations. GLSKG can provide not only external entity information for the semantic dependency parser (SDP) but also provide semantic dependency information for the SDP to improve its performance.
A Local Descriptor for Age Invariant Face Recognition under Uncontrolled Environment

Face recognition has major applications such as missing people or children identification or criminal identification. These applications become more challenging while having age variation. To handle the above applications, an age invariant face recognition system is required. This paper proposes a robust descriptor which works on local region of face parts and extract the discriminative features to identify the people. In the first step, face detection, alignment and cropping is done, secondly, proposed discriminative descriptor is utilized over the local regions of eye, nose and mouth parts. In the third step, a machine learning method -Support Vector machine is utilized for the classification. Finally, Rank-1 recognition accuracy is computed with 5-fold method. To measure the effectiveness of the proposed descriptor, a standard and challenging dataset has been utilized. The effectiveness of the descriptor has been proved in the result section and proposed descriptor outperformed to the existing descriptor based approaches.
eSports – 'It's in the Game': The Naissance of a new Field of International Arbitration

As the Electronic Sports (eSports) scene is becoming more and more important and championships are paying out ever larger amounts of prize money (up to USD 100 million), the eSports scene is confronted with similar problems as traditional sports: doping, match fixing, labour disputes of team members, as well as unfair conduct (aka cheating or eDoping). All these problems are adequately addressed in the traditional sport disciplines by arbitration courts of the corresponding associations and the Court of Sports Arbitration (CAS).In eSports, however, the corresponding instances are absent or are currently only being set up, and the necessary support from the eSports community is missing. A few legal scholars pronounced the idea that eSports, to solve its looming disputes, is in need for arbitration, and given the uniqueness of the games industry, in need for specialists. These authors also called for an eSports Arbitration Court, resembling the CAS, which would have a more thorough understanding of specific games and the applicable rules so as to reach a reasonable decision.
Trend Analysis and Predictive Modeling Using Machine Learning Models on Indian Election Historical Dataset

India has been among the largest democracy around the world, and democratic election process has played an important role in achieving this status. Predicting elections result is an important factor as it influences the market situation at local and global level. This paper had focused on data analytics and machine learning using Python, on historical dataset. Historical datasets of Indian Parliamentary Elections have been taken for a larger time span of 1977–2014 and results of same have been plotted using Python. In this paper, five machine learning models have been used for predicting win or loss for a seat in an election. The models primarily used for this analysis are Gaussian Naive Bayes, extra tree classifier, K-nearest neighbors classifier (KNN), logistic regression and decision tree classifier. In result section, results of five model used for study were evaluated and compared. This study had depicted that decision tree classifier provided comparatively good accuracy score among the chosen five models.
On the Prospects of Increasing Publication Activity in the ‘Informatization of Education’ Field

In this communication, I briefly analyze the consequences of the Russian government's unrealistic policy concerning researchers' publication output. Provided a variety of publication opportunities, including repositories of research data and preprints, articles in scientific journals and conference proceedings remain to be the main option. Significant risks are involved when aiming to fulfil the government's requirements at all costs, including the use of unethical services. Thus, brokers forward their customers' articles to non-peer-reviewed journals, which are regularly discontinued in citation databases. It is noted that conference proceedings remain to be the main source of scientific information in computer sciences. In this study, a thematic search across the ‘informatization of education’ field in Scopus was conducted to compare the publication dynamics of Russian and Chinese authors. A significant difference was observed between the countries both in terms of the thematic scope of conducted research and publication sources. It is concluded that Russian science managers should study the experience of foreign countries. For example, China underwent a period of excessive reliance on the quantitative instruments in measuring research performance, which resulted in ethical violations and multiple retractions. Recommendations are given for improving the ranking and recognition of Russian researchers among the international scientific community.
A novel SCA-resilience flip-flop design utilizing the current mode logic based on the three-independent-gate field effect transistors

In this paper, current mode logic based on the three-independent-gate field effect transistor (TIGFET) is introduced as the circuit-level side-channel attack (SCA) countermeasures, and a SCA-resilience flip-flop (DyCML) is designed to make the power consumption constant. Then, a simplified advanced encryption system (AES) is built, and power analysis is performed to evaluate the SCA-resistance efficacy. Simulation results show that the key with the TIGFET-based DyCML is not revealed with 255 power traces. The proposed design occupies less area usage and requires less delay overhead compared to the original TIGFET-based true single-phase clock (TSPC) and modified TSPC (mTSPC).
Saliency Prediction with Relation-Aware Global Attention Module

The deep learning method has achieved great success in saliency prediction task. Like depth and depth, the attention mechanism has been proved to be effective in enhancing the performance of Convolutional Neural Network (CNNs) in many studies. In this paper, we propose a new architecture that combines encoder-decoder architecture, multi-level integration, relation-aware global attention module. The encoder-decoder architecture is the main structure to extract deeper features. The multi-level integration constructs an asymmetric path that avoid information loss. The Relation-aware Global Attention module is used to enhance the network both channel-wise and spatial-wise. The architecture is trained and tested on SALICON 2017 benchmark and obtain competitive results compared with related research.
RETRACTED: Security Protection of Information Utilizing Halfway Speculation

Abstract Securing privacy in information-digging (data mining) has become indispensable for buying and selling labelled statistics in statistical evaluation, validation, and guide. In such cases, records are either released in published form for re(use) through others for research functions. Here, privacy takes an extensive role to secure the records from ever-escalating net phishing and different probable attackers. Significant upgrades for strong privateness upkeep and protection have emerged as mandatory. In this paper, we endorse the approach of partial speculation. This technique is essential to mask the information in this sort of manner that different facts mining strategies can be without difficulty applied to it. On the alternative hand, it makes the information okay-nameless for this reason, information cannot be re-identified at the same time as records remain almost useful. In partial speculation, simplest identifiers that are vital are speculated first. If this step, safeguards the records from external information linkage, then the technique no longer speculates besides, preventing extra speculation than important with minimal facts loss.
Current Issues of Collection, Verification and Evaluation of Evidence in Prediction Proceedings in Criminal Cases

The topic of collecting, checking and evaluating material evidence in pre-trial criminal proceedings is one of the broadest and most complex in criminal proceedings.
Confidentiality Guaranteed E-Health Treatment for Cloud Cover Enhanced Systems Based on IoT

Abstract The advances made with Internet-of- Things (IoTs) on networking technologies and hardware has made for an at the moment more relaxed human life. In addition to the so -called “intelligent” settings, e-healthcare services, as well as real-time diagnoses and diagnostic consulting, have increasingly been integrated into IoT. Cloud cover layers have also been hired, and have demonstrated their utility by offering quick response time and low latency to increase the capability of IoT based health systems. Such a development, however, retains the privacy of users as a serious obstacle, to some degree solving security/privacy problems. Such technology still had less privacy controls when it was at a childhood stage. Therefore, our latest work is on an eHealth System for the security of privacy problems in electronic medical reports (EMR). In addition, we have interacted with and compared work on the proposed work in terms of reaction time and delay. The findings demonstrate the complete work in conjunction with normal network parameters is effective to provide privacy.
Notes about the linear complexity of cyclotomic binary sequences of order four over a finite field of odd characteristic

Abstract In this paper, we investigate the linear complexity and the minimal polynomial of cyclotomic binary sequences of order four over a finite field of odd characteristic. The sequences considered are determined on the basis of two cyclotomic classes of the fourth order. We show that they have a high linear complexity.
An Innovative Concept for the Construction of Short Basic Math Courses

The paper discusses an innovative concept of building a course in teaching mathematical disciplines for students of non-mathematical specialties. A mechanism has been developed that makes it possible to significantly modernize the educational methodology of presenting the course of mathematical analysis. A system of filling the didactic and methodological support of the course is proposed, including modern technologies of distance, electronic and online learning. The use of computer task generators and modern computer mathematical packages in the practice of teaching mathematics at universities is considered. The inclusion of informational content raises the level of the traditional course presentation, taking it to a higher level of interaction between the lecturer and the audience. The practical use of the proposed concept contributes to the creative development and professional growth of teachers.
Construction of reliable image captioning system for web camera based traffic analysis on road transport application

The automated captioning of natural images with appropriate descriptions is an intriguing and complicated task in the field of image processing. On the other hand, Deep learning, which combines computer vision with natural language, has emerged in recent years. Image emphasization is a record file representation that allows a computer to understand the visual information of an image in one or more words. When it comes to connecting high-quality images, the expressive process not only requires the credentials of the primary item and scene but also the ability to analyse the status, physical characteristics, and connections. Many traditional algorithms substitute the image to the front image. The image characteristics are dynamic depending on the ambient condition of natural photographs. Image processing techniques fail to extract several characteristics from the specified image. Nonetheless, four properties from the images are accurately described by using our proposed technique. Based on the various filtering layers in the convolutional neural network (CNN), it is an advantage to extract different characteristics. The caption for the image is based on long short term memory (LSTM), which comes under recurrent neural network. In addition, the precise subtitling is compared to current conventional techniques of image processing and different deep learning models. The proposed method is performing well in natural images and web camera based images for traffic analysis. Besides, the proposed algorithm leverages good accuracy and reliable image captioning.
Research Data Management Services in Tertiary Institutions in Zimbabwe

The chapter seeks to analyze how librarians in Zimbabwe are responding to increasing librarian roles in the provision of research data services. The study sought to ascertain librarians' awareness and preparedness to offer research data management services at their institutions and determine support required by librarians to effectively deliver research data services. Participants were invited to respond to the survey, and survey monkey was used to administer the online questionnaire. The collected data was analyzed using content analysis, and it was thematically presented. Findings revealed that librarians in Zimbabwe are aware of their role in research data management, but the majority are not prepared to offer research data management services due to a lack of the required skills and resources. Challenges that were noted include lack of research data management policy at institutional levels and information technology issues such as obsolescence and security issues.
Orthogonally Interweaved Data Encryption Method for Screen to Camera Communication

Due to the broadcast nature of screen to camera communication (SCC) channels, SCC systems are under threat of eavesdropping. In this paper, we propose an orthogonally interweaved data encryption method to enhance the security of SCC systems. In our design, the secret key sequence is arrayed randomly and then used to generate an orthogonal key matrix by interweaving the rows in the identity matrix. By multiplying the key matrix, the user data bits are orthogonally interweaved. Then the key sequence is transmitted together with the user data after color-code modulation. The legitimate receiver's camera will subsequently demodulate the color code and extract the secret key sequence from the captured images. Finally, the orthogonal key matrix can be recovered based on this secret key sequence and its transpose can be directly used to decrypt the data bits. Under this method, even though the eavesdropper can capture the color coded image and demodulate the secret key sequence, it can not retrieve the transmitted data without the key matrix generation rule and the orthogonally interweaved data encryption scheme. Additionally, we analyze the theoretical information leakage of an SCC system encrypted using the proposed method. We perform simulations and illustrate that the proposed encryption method can achieve high security performance.
An assessment of noise variance estimations in Bayes threshold denoising under stationary wavelet domain on brain lesions and tumor MRIs

Purpose A major key success factor regarding proficient Bayes threshold denoising refers to noise variance estimation. This paper focuses on assessing different noise variance estimations in three Bayes threshold models on two different characteristic brain lesions/tumor magnetic resonance imaging (MRIs). Design/methodology/approach Here, three Bayes threshold denoising models based on different noise variance estimations under the stationary wavelet transforms (SWT) domain are mainly assessed, compared to state-of-the-art non-local means (NLMs). Each of those three models, namely D1, GB and DR models, respectively, depends on the most detail wavelet subband at the first resolution level, on the entirely global detail subbands and on the detail subband in each direction/resolution. Explicit and implicit denoising performance are consecutively assessed by threshold denoising and segmentation identification results. Findings Implicit performance assessment points the first–second best accuracy, 0.9181 and 0.9048 Dice similarity coefficient (Dice), sequentially yielded by GB and DR; reliability is indicated by 45.66% Dice dropping of DR, compared against 53.38, 61.03 and 35.48% of D1 GB and NLMs, when increasing 0.2 to 0.9 noise level on brain lesions MRI. For brain tumor MRI under 0.2 noise level, it denotes the best accuracy of 0.9592 Dice, resulted by DR; however, 8.09% Dice dropping of DR, relative to 6.72%, 8.85 and 39.36% of D1, GB and NLMs is denoted. The lowest explicit and implicit denoising performances of NLMs are obviously pointed. Research limitations/implications A future improvement of denoising performance possibly refers to creating a semi-supervised denoising conjunction model. Such model utilizes the denoised MRIs, resulted by DR and D1 thresholding model as uncorrupted image version along with the noisy MRIs, representing corrupted version ones during autoencoder training phase, to reconstruct the original clean image. Practical implications This paper should be of interest to readers in the areas of technologies of computing and information science, including data science and applications, computational health informatics, especially applied as a decision support tool for medical image processing. Originality/value In most cases, DR and D1 provide the first–second best implicit performances in terms of accuracy and reliability on both simulated, low-detail small-size region-of-interest (ROI) brain lesions and realistic, high-detail large-size ROI brain tumor MRIs.
Lisk Restaurant sidechain, evolution from Lisk SDK 2.3.8 to 5.0.3, evaluation of improvements in performance and security

In this paper, we evaluate the benefits of the Lisk SDK version, 5.0.3, over the old version of Lisk SDK 2.3.8 using the Restaurant sidechain solution created as a Proof-Of-Concept of online food requests using Lisk blockchain technology. We evaluated the Restaurant sidechain in aspects of performance, the capacity of receiving transactions in a short amount of time, the maximum waiting time for customers requesting food, consensus improvements of Lisk-BFT, and transaction spam attack resilience. It was observed the aspects of block size, transaction pool size, communication between nodes, and analyzed security concerns regarding liveness and Denial of Service attacks in the sidechain. In the end, we concluded that the new version of Lisk SDK 5.0 demonstrated improvements in size capacity of a block, and therefore improved Lisk Restaurant capacity to receive customers food requests, also transaction pool size improvement, and the creation of a mitigation strategy implemented on node life cycle reduced the chance of interruption on the entire network during a transaction spam attack.
Realtime Facial Expression Recognition using Deep Learning

Facial expression is the key to determine the physiological and psychological behaviour of any subject or crowd. Although it is easier for human beings to determine facial expressions, it's certainly a challenging task for machines. Automatic facial emotion recognition (FER) is an emerging research area in image classification. The availability of data as well as the ease of processing has opened up new possibilities of research with better accuracy. With the increase in computing capability deep learning processes have become a highly suitable technique in determining FER. As part of this paper, we have developed a deep convolutional neural network (CNN) which is used to classify the input images into seven different emotions as part of the study. We have used the FER2013 dataset images from the Kaggle website [1] to train our model and testing is done using still images as well as real-time surveillance data obtained from a webcam. A simple UI is used to display the test subject's real-time video along with the predicted labels during the time of video capture. The proposed neural networks are implemented using TensorFlow.