Reviewerly/exHarmony huggingface dataset:
The study of the subjective prerequisites for a healthy lifestyle in students of educational institutions

The article substantiates the urgency of the problem of forming healthy lifestyle skills in students of general educational organizations. The authors proceed from the recognition of the leading role of a healthy lifestyle in the system of factors affecting human health, as well as from the requirements of the Federal state educational standards for personal educational results in the field of preservation and promotion of health. This study is devoted to an integrated assessment of the subjective prerequisites for a healthy lifestyle in students of general educational organizations. The article presents the results of a survey of students regarding their relationship to a healthy lifestyle in dynamics over a three-year period. The statistical data on the subjective prerequisites of a healthy lifestyle — ideas, motivation, behavior, which together determine the attitude to a healthy lifestyle as an integral phenomenon, is analyzed. The authors emphasize the interconnection and mutual influence of the subjective prerequisites of healthy lifestyle on the formation of sustainable habits of health-preserving behavior. The method of comparative analysis revealed the contradictions between knowledge and motivation, knowledge and behavior, motivation and behavior. The subjective psychological prerequisites for a healthy lifestyle are not consistent and contradict each other, which negatively affects the formation of healthy lifestyle habits. The ideas about the requirements of a healthy lifestyle are largely socially desirable and reflect the cognitive patterns of a healthy lifestyle transmitted in the sociocultural environment of children. The value of one’s own health is not realized and is not lived, and, as a result, understanding the importance of health and a healthy lifestyle does not contribute to the consolidation of conscious, focused and sustainable health-preserving behavior.
Antecedents of Soft-Skills in Higher Education Institutions of Saudi Arabia Study under COVID-19 Pandemic

Higher Education Institutions (HEIs) play an important role in development of productive academic environment for university graduates.Similarly, soft skills are an essential requirement for fresh graduates to provide and prepare them for the convenient and suitable job market.In last few months, emerging situation of COVID-19 has affected the Saudi higher education sector at large scale from business perspective by diverting the regular studies to online classes.Online classes have made it more difficult for academic staff to cope up with this deteriorating situation.This study is planned to identify numerous soft-skills, which could be important to conduct academic activities under COVID-19 crisis.Study will make an analysis focusing on the importance of soft-skills in higher education institutions before and after the COVID-19 crisis.Study collected data from both public and private sector higher educational institutions located in Jeddah, Saudi Arabia.Study observed the key role and overall impact of soft-skills to enhance the learning aptitudes under uncertain COVID-19 situation.Study will also focus on the importance of soft-skills in job success in the Saudi job market for university graduates.Study will also focus on the importance of soft-skills integration in higher education curriculum.Study will help the decision makers to plan and prepare accordingly for developing academic environment to get better job opportunities in the competitive Saudi business organizations.
Robust Neural Machine Translation with ASR Errors

In many practical applications, neural machine translation systems have to deal with the input from automatic speech recognition (ASR) systems which may contain a certain number of errors.This leads to two problems which degrade translation performance.One is the discrepancy between the training and testing data and the other is the translation error caused by the input errors may ruin the whole translation.In this paper, we propose a method to handle the two problems so as to generate robust translation to ASR errors.First, we simulate ASR errors in the training data so that the data distribution in the training and test is consistent.Second, we focus on ASR errors on homophone words and words with similar pronunciation and make use of their pronunciation information to help the translation model to recover from the input errors.Experiments on two Chinese-English data sets show that our method is more robust to input errors and can outperform the strong Transformer baseline significantly.
Scientific Text Mining and Knowledge Graphs

Unstructured scientific text, in various forms of textual artifacts, including manuscripts, publications, patents, and proposals, is used to store the tremendous wealth of knowledge discovered after weeks, months, and years, developing hypotheses, working in the lab or clinic, and analyzing results. A grand challenge on data mining research is to develop effective methods for transforming the scientific text into well-structured forms (e.g., ontology, taxonomy, knowledge graphs), so that machine intelligent systems can build on them for hypothesis generation and validation. In this tutorial, we provide a comprehensive overview on recent research and development in this direction. First, we introduce a series of text mining methods that extract phrases, entities, scientific concepts, relations, claims, and experimental evidence. Then we discuss methods that construct and learn from scientific knowledge graphs for accurate search, document classification, and exploratory analysis. Specifically, we focus on scalable, effective, weakly supervised methods that work on text in sciences (e.g., chemistry, biology).
An Explanatory Review on Cybersecurity Capability Maturity Models

Cybersecurity is growing exponentially day by day in both the public and private sectors.This growth also comes with a new and dynamic cyber-threats risk that causes both sectors' performance to halt.These sectors must update their cybersecurity measures and must understand the capability and maturity of their organization's cybersecurity preparedness.Cybersecurity maturity models are widely used to measure how ready an organization is when it comes to cybersecurity.The main aim of this article is to conduct a comprehensive review of the current cybersecurity capability maturity models using a systematic review of published articles from 2011 to 2019.A comparative study was conducted based on Halvorsen and Conradi's taxonomy.The review indicated almost all the cybersecurity maturity model consists of similar elements like maturity levels and processes but significantly lacks the validation process, it was observed each of the models were predominantly designed for a specific purpose and also for different organization size and application domain.
Explainable Artificial Intelligence for Digital Forensics: Opportunities, Challenges and a Drug Testing Case Study

Forensic analysis is typically a complex and time-consuming process requiring forensic investigators to collect and analyse different pieces of evidence to arrive at a solid recommendation. Our interest lies in forensic drug testing, where evidence comprises a multitude of experimentally obtained data from samples (e.g. hair or nails), occasionally combined with questionnaire data, with a goal of quantifying the likelihood of drug use. The availability of intelligent data-driven technologies can support holistic decision-making in such scenarios, but this needs to be done in a transparent fashion (as opposed to using black-box models). To this end, this book chapter investigates the opportunities and challenges of developing interactive and eXplainable Artificial Intelligence (XAI) systems to support digital forensics and automate the decision-making process to enable fast and reliable generation of evidence for the court of law. Relevant XAI techniques and their applications in forensic testing, including feature section, missing data handling, XAI for multi-criteria and interactive learning, are discussed in detail. A case study on a forensic science company is used to demonstrate the real challenges of forensic reporting and potential for making use of forensic data to pave the way for future research towards XAI-driven digital forensics.
USEFULNESS OF MOBILE TEACHING AND LEARNING AS PERCEIVED BY ROMANIAN AND LITHUANIAN SCIENCE TEACHERS

With the explosion of mobile technologies, mobile teaching and learning became an important educational challenge. Since both teachers and students are increasingly using mobile devices in their everyday life, the question is why so little progress has been made in this direction. Clearly, the initiative of introducing mobile technology in class should come from teachers. Existing research shows a large diversity of factors that are influencing their perception as regards the usefulness and opportunity of mobile teaching and learning. This research aims to understand the extent to which the expectancy of increased students’ motivation and better understanding are influencing the perception of mobile teaching usefulness. A structural model has been developed and tested on two samples of science education teachers, one from Romania and the other from Lithuania. The results show that the expectancy of a better understanding of lessons by students has an important effect on the teachers’ perception of mobile teaching and learning. Keywords: educational usefulness, learning motivation, mobile teaching, mobile learning, pilot research
Adaptive density peak clustering based on dimensional-free and reverse k-nearest neighbors

Cluster analysis plays a crucial component in consumer behavior segment. The density peak clustering algorithm (DPC) is a novel density-based clustering method. However, it performs poorly in high-dimension datasets and the local density for boundary points. In addition, its fault tolerance is affected by one-step allocation strategy. To overcome these disadvantages, an adaptive density peak clustering algorithm based on dimensional-free and reverse k-nearest neighbors (ERK-DPC) is proposed in this paper. First, we compute Euler cosine distance to obtain the similarity of sample points in high-dimension datasets. Then, the adaptive local density formula is used to measure the local density of each point. Finally, the reverse k-nearest neighbor idea is added on two-step allocation strategy, which assigns the remaining points accurately and effectively. The proposed clustering algorithm is experiments on several benchmark datasets and real-world datasets. By comparing the benchmarks, the results demonstrate that the ERK-DPC algorithm superior to some state-of- the-art methods.
Introduction to the special section on blockchain techniques for the internet of vehicles security (VSI-bciov)

As an application of Internet of Things (IoT) technology, the Internet of Vehicles (IoV) faces two main security issues: (1) the central server of the IoV may not be powerful enough to support the centralized authentication of the rapidly increasing connected vehicles, (2) the IoV itself may not be robust enough to single-node attacks. To address these issues, in this paper, we propose SG-PBFT (Score Grouping-PBFT), a secure and efficient distributed consensus algorithm for blockchain applications in the IoV. The distributed structure can reduce the pressure on the central server and decrease the risk of single-node attacks. The SG-PBFT consensus algorithm improves the traditional practical Byzantine fault tolerance (PBFT) consensus algorithm by optimizing the PBFT consensus process and using a score grouping mechanism to achieve a higher consensus efficiency. The experimental results show that the method can greatly improve the consistency efficiency and effectively prevent single-node attacks. Specifically, when the number of consensus nodes reaches 1000, the consensus time of our algorithm is only about 27% of what is required for the state-of-the-art PBFT consensus algorithm. Our proposed SG-PBFT algorithm is versatile and can be used in other application scenarios which require high consensus efficiency.
MoGCN: Mixture of Gated Convolutional Neural Network for Named Entity Recognition of Chinese Historical Texts

Named Entity Recognition (NER) systems have been largely advanced by deep neural networks in the recent decade. However, the state-of-the-arts on NER have been less applied to Chinese historical texts due to the lack of standard corpora in Chinese historical domains and the difficulty of accessing a quality ancient corpus. This paper addresses the respective issues and proposes an efficient automatic processing solution for tackling NER of ancient Chinese data, including the implementation of data-driven tagging and an innovative end-to-end network namely “MoGCN” (Mixture of Gated Convolutional Neural Network). A corpus consisting of three genres of Chinese historical classics is generated by our tagging approach, which is experimented for uncovering the generalization ability of proposed model. The empirical analysis demonstrates that our proposed model achieves the best results with above 1.5% F1-score improvement over other sophisticated models in this dataset, where the experimental performance shows positive dependence on the quality of corpus. Furthermore, our model can perform much better on shorter entities especially for 2-charater ones, while many long-range entities can be only identified by our model based on our auxiliary attribute analysis. This work serves as a preliminary exploitation of NER for historical data, providing unique insights and reference values for similar tasks. Future work should be focused on more exploration about NER optimization on massive Chinese traditional texts with linguistic features and learning strategies.
An Empirical Evaluation of Bitcoin Price Prediction Using Time Series Analysis and Roll Over

Bitcoin has attracted considerable attention in today’s world because of the combination of encryption technology along with the monetary units. For traders, Bitcoin leads to a promising investment because of its highly fluctuating price. Block chain technology assists in the transactions of documentation. The characteristics of the bitcoin which is derived from the blockchain technology has led to diverse interests in the field of economics. The bitcoin data is selected from 2013 to 2018, over a period of 5 years for this analysis. Here a new roll over technology is applied where new data is obtained over time which will close out the old information during machine training. This mechanism will help in incorporating new information in the short-term learning. The results show that the rollover mechanism improves the time series prediction accuracy.
A Fast and Robust BERT-based Dialogue State Tracker for Schema-Guided Dialogue Dataset

Dialog State Tracking (DST) is one of the most crucial modules for goal-oriented dialogue systems. In this paper, we introduce FastSGT (Fast Schema Guided Tracker), a fast and robust BERT-based model for state tracking in goal-oriented dialogue systems. The proposed model is designed for the Schema-Guided Dialogue (SGD) dataset which contains natural language descriptions for all the entities including user intents, services, and slots. The model incorporates two carry-over procedures for handling the extraction of the values not explicitly mentioned in the current user utterance. It also uses multi-head attention projections in some of the decoders to have a better modelling of the encoder outputs. In the conducted experiments we compared FastSGT to the baseline model for the SGD dataset. Our model keeps the efficiency in terms of computational and memory consumption while improving the accuracy significantly. Additionally, we present ablation studies measuring the impact of different parts of the model on its performance. We also show the effectiveness of data augmentation for improving the accuracy without increasing the amount of computational resources.
Fast entangling gates in long ion chains

We present a model for implementing fast entangling gates (${\sim}1~\mu$s) with ultra-fast pulses in arbitrarily long ion chains, that requires low numbers of pulses and can be implemented with laser repetition rates well within experimental capability. We demonstrate that we are able to optimise pulse sequences that have theoretical fidelities above $99.99\%$ in arbitrarily long ion-chains, for laser repetition rates on the order of $100-300$~MHz. Notably, we find higher repetition rates are not required for gates in longer ion chains, which is in contrast to scaling analyses with other gate schemes. When pulse imperfections are considered in our calculations, we find that achievable gate fidelity is independent of the number of ions in the chain. We also show that pulse control requirements do not scale up with the number of ions. We find that population transfer efficiencies of above $99.9\%$ from individual ultra-fast pulses is the threshold for realising high-fidelity gates, which may be achievable in near-future experiments.
The Effects of App-Delivered Cognitive Behavioral Therapy for Insomnia (CBT-I) on Sleep Quality, Dysfunctional Beliefs, and Sleep Hygiene

is trained as a social psychologist and his research encompasses social, health, and pedagogical psychology
An SLR-tool: search process in practice

Systematic Literature Reviews (SLRs) have established themselves as a method in the field of software engineering. The aim of an SLR is to systematically analyze existing literature in order to answer a research question. In this paper, we present a tool to support an SLR process. The main focus of the SLR tool (https://www.slr-tool.com/) is to create and manage an SLR project, to import search results from search engines, and to manage search results by including or excluding each paper. A demo video of our SLR tool is available at https://youtu.be/Jan8JbwiE4k.
Identification of Co-change Patterns in Software Evolution

Tracking the after-effect of changes is very difficult due to complex and huge structure of software applications now days. Researchers have found that revision history is very useful in deriving dependencies in software applications. However it is difficult to choose the right history length that is useful for dependency identification. Also all the new and old commits do not have the same potential in identifying dependencies. In this paper, we propose the use of change history for measuring the co-change probability in software applications by varying the weightage of change commits. Academicians and researchers may find this approach useful for studying the effect of revision age on cochange prediction results.
Public libraries on Facebook: content analysis of Turkish public libraries' posts

Purpose The study aims to analyze the posts shared by public libraries in Turkey on their official Facebook pages. The targets of the study are to detect content categories shared by the libraries and to describe user interaction of libraries on the basis of their posted content. Design/methodology/approach The study examined data relating to 23,027 posts shared by 116 public libraries through descriptive and content analyses. The content analysis on the posts identified general topic and sub-topics through both machine-tagging and checks by the authors. Turkish word parsing applications and stop lists were employed in the designation of sub-categories. In this process, word clusters were identified to allow for the designation of sub-topic headings through the use of the Latent Dirichlet Allocation (LDA) technique. Findings The present study identified that a significant portion of the 116 public libraries in Turkey with official Facebook pages had started to use this platform in the year 2017. According to the findings, the user interaction of libraries generally occurs in the form of likes. Analyses on the topic categories of the posts established that a remarkable number of public libraries in Turkey targeted users from pre-school children to young adults. Originality/value The study contributes to the existing body of literature as one of the first research studies describing the current situation concerning public libraries in Turkey through the analysis of the content they generate on Facebook pages.
Local Path Planning Based on an Improved Dynamic Window Approach in ROS

We consider the problem of robot local path planning using traditional dynamic window approach based ROS. By means of an improved dynamic window approach, we are able to reduce the complexity of the problem and provide a practically efficient procedure for its solution. Improved dynamic window approach based on robot dynamics model is introduced and described in the paper. By abandoning the trajectory that the velocity reduces to zero when it encounters an obstacle to simplify the search speed space. This algorithm improves the real-time performance and reduces the computational complexity of the algorithm.
The Perks of Using Machine Learning for QoT Estimation with Uncertain Network Parameters

We compare the resilience to network parameters uncertainty between a GN-model based QoT tool and ML-based ones. We study the mean square error of the estimated G-OSNR and a novel metric based on the overestimation probability.
Game-based Methods for Competence “Teamwork and Leadership” Development: The Case of Engineering Students

The competence “teamwork and leadership” is one of the key and the most required in the modern professional environment. The article presents the experience of the Department of sociology, psychology and social management of Moscow Aviation Institute in the development of teamwork skills among students majoring in engineering. Teamwork in the article is understood as an effective joint activity to achieve goals. Teamwork as a professional competence includes certain skills and abilities that are being formed gradually on the basis of interpersonal and intergroup communication. For the formation of team qualities, gamebased learning methods are used, which increase the effectiveness of pedagogical efforts. Game methods in comparison with traditional lectures and seminars involve more active communication, emotional colouring and visual demonstration of the results. The main task of a teacher is not to broadcast information and check its assimilation, but to stimulate the activity of students themselves. Game-based methods are both a diagnostic and corrective tool, they allow to identify already existing skills, activate the behaviour of students in the desired direction and consolidate the result. The article analyses the experience of using active training methods for the development of the competence “teamwork and leadership”. Trainings are conducted in several stages, on each of them certain tasks are being solved: the current level of competence development is evaluated, tasks are performed together, results are discussed, and an exercise is given to reinforce the skill. After the trainings, change in the participants’ attitude to teamwork and increase in the level of cohesion, trust, and involvement were revealed. The experience of organising of trainings turned out to be successful and useful not only for building competence, but also for identifying difficulties that prevent students from creating effective teams. The materials of the article may be of interest to University teachers working on the development of students’ competence “teamwork” and using learning technologies.