Reviewerly/exHarmony huggingface dataset:
A Moderated Mediation Effect of Online Time Spent on Internet Content Awareness, Perceived Online Hate Speech and Helping Attitudes Disposal of Bystanders

Digital counter narratives have proved to be a consistent support to victims of online speech as they are defined as online narrative activities and actions, mostly verbal that have as effect the lowering of the negative psychological distress caused by hate speech (HS) over their victims. Internet content awareness (ICA) has an indirect significant effect on the relationship between perceived online HS and helping attitudes disposal towards the victim of HS, moderated by the online time spent by youth. Does internet content awareness (ICA) influence the helping attitudes disposal of bystanders in an online hate speech situation? Is the indirect effect of ICA mediation significant in triggering the helping attitudes towards the victims? Does the online time spent by youth moderates this relationship? These are the questions proposed by this research funded by Erasmus+ project Hate’s Journey. Our research team has designed an online questionnaire addressing 206 youth from Latvia, Turkey, Spain and Romania. We have used a moderated mediation model (Model 7 from Process Hayes). The key finding of this research is that in an online hate speech situation, the more aware a person is regarding the online content, the more helping attitudes will show towards the victim of HS, under the conditioning of less and medium time spent online. Conclusions and discussions will focus on the argumentation of results with regards to emergent behaviour paradigm.
Natural language processing for analysis of student online sentiment in a postgraduate program

Higher education institutes are continually looking for new and better ways to support and understand the learning experience of their students. One possible option is to use sentiment analysis tools to investigate the attitudes and emotions of students when they are interacting on social media about their course experience. In this study, we analysed the social media posts, from a closed programme-based community, of more than 300 students in a single programme cohort by processing the dataset with the Google cloud-based Natural Language Processing API for sentiment analysis. The sentiment scores and magnitudes were then visualised to help explore the research question ‘How does a natural language processing tool help analyse student online sentiment in a postgraduate program?’ The results have provided a better understanding of students’ online sentiment relating to the activities and assessments of the programme as well as the variation of that sentiment over the timeline of the programme.
Simulation of a Real Call for Research Projects as Activity to Acquire Research Skills: Perception Analysis of Teacher Candidates

In this research, a novel methodology based on the simulation of a call for research projects was applied for the training of STEM secondary school teachers, with results raised and analyzed to determine the response of the students to this new methodology. The activity was applied in the same course during two academic years with student groups from very different teaching specialties such as mathematics, physics and chemistry, biology and geology, technology and health processes who were studying the Master’s Degree in Secondary Education, specifically, the 3 European Credit Transfer and Accumulation System (ECTS) course of Initiation to Educational Research (IER), this Master’s course being mandatory for working as a secondary professor. The Master’s students are asked to write their own research project proposals for a fictitious call on a topic freely chosen by them, which might have been related to the research line of the final Master’s thesis. In it, they had to propose all the contents studied in the course (such as writing a brief state of the art, establishing a research team, setting objectives, a description of the methodology for educational research, instruments, a plan for the dissemination of the results, the needed resources, etc.). The students’ perceptions of the usefulness and reality of what they had learned for their professional development and for writing their final theses were assessed. The results based on the perceptions of the students demonstrate that the activity had been useful for assimilating concepts related to educational research in the context of secondary education (research skills), which will be useful for improving the critical sense of the students (teacher candidates) and for their professional future in the context of applied research in day-to-day secondary teacher activities. Furthermore, the results show the activity was useful for the development of the final Master’s thesis. The difficult aspects that the activity presented for them were analyzed. The results were statistically compared for the students of the different specialties, deducing, in all cases, a homogeneous good acceptance with slight differences between them.
A Parallel Convolutional Neural Network for Pedestrian Detection

Pedestrian detection is a crucial task in many vision-based applications, such as video surveillance, human activity analysis and autonomous driving. Recently, most of the existing pedestrian detection frameworks only focus on the detection accuracy or model parameters. However, how to balance the detection accuracy and model parameters, is still an open problem for the practical application of pedestrian detection. In this paper, we propose a parallel, lightweight framework for pedestrian detection, named ParallelNet. ParallelNet consists of four branches, each of them learns different high-level semantic features. We fused them into one feature map as the final feature representation. Subsequently, the Fire module, which includes Squeeze and Expand parts, is employed for reducing the model parameters. Here, we replace some convolution modules in the backbone with Fire modules. Finally, the focal loss is led into the ParallelNet for end-to-end training. Experimental results on the Caltech–Zhang dataset and KITTI dataset show that: Compared with the single-branch network, such as ResNet and SqueezeNet, ParallelNet has improved detection accuracy with fewer model parameters and lower Giga Floating Point Operations (GFLOPs).
Implementasi Kolaborasi Orang Tua dan Guru Dalam Pelaksanaan Pembelajaran Daring pada PAUD

In current conditions it is highly recommended for all educational institutions to carry out online or online-based learning. Online implementation is carried out without meeting face to face with students. However, online implementation in PAUD really requires active participation from parents because of the condition of students who have not fully been able to access the media in online implementation. The purpose of this study is to describe online learning based on collaboration between parents and teachers in early childhood education. The method in this research is using descriptive qualitative research. The results of this study are expected that parents will be more motivated to take a proactive role in the learning process of children through online learning based on collaboration between parents and teachers.
THE PRELIMINARY GEOTOURISM STUDY IN PHETCAHBUN PROVINCE, THAILAND

Phetchabun Province is situated in the lower northern region of Thailand.This province has many natural attractions with interesting cultures and traditions as a popular tourism destination.The geological signification of this area is in the collision zone between Shan-Thai plate and Indochina plate.Resulting in many outstanding geological diversities.This research focuses on geological features or ge osites as natural tourist attractions.Sixteen geosites were selected in Phetchabun Province consisting of waterfalls, potholes, cliffs, caves, columnar jointing, fossils localities, and many scenic viewpoints arising from tectonic plate evolution, weathering and erosion.Guidelines for geotourism development were analyzed.This is the first geotourism research study in this area, and presents geological data, recommended travel routes, an overview of the current situation and possible problems of promoting geosites as tourist attractions.Sustainable development of geotourism in Phetchabun Province will provide employment opportunities for the local population, respond to the currently increasing global ecotourism sector and increase geopark potential to be a national geopark.
MODELLING MALAYSIAN GOLD PRICES USING GEOMETRIC BROWNIAN MOTION MODEL

Heat and mass transfer effects of Casson fluid in the entrance of concentric annuli with moviment of inner wall was analyzed here.The problem analysis concerns the simultaneous development of thermal boundary layers and hydrodynamic in concentric walls, one ring is isothermal and the other wall being adiabatic.With the assumption that the inner ring rotates with a fixed angular velocity, also the outer ring is at rest.The finite difference technique is applied to find the velocity Profiles, variation of pressure in the radial coordinate direction and temperature changes in the same direction.Calculation results are obtained for different annular gap values, Casson number and Prandtl's number.The comparison of the results for different special cases was made and observed.
Applying Two-Dimensional Piecewise-Polynomial Basis for Medical Image Processing

In this paper, determining the recovery coefficients outcome of digital processing of radiographic images by using two-dimensional piece-polynomial basis, image compression and algorithms have been developed that is quality imaging compared to the first image processing.We know that one of the main problem on image compression is to find and apply an effective method that allows each type of pixel to be displayed in a compact form.In order to solve this problem, two-dimensional piece-polynomial basis are used, and as a result, the compression and recovery coefficients of the image were determined.
Anchoring the need to revise cross-border access to e-evidence

In April 2018 the European Commission presented an e-evidence package including a Proposal for a Regulation on a European Production and Preservation Orders for electronic evidence in criminal matters and a Proposal for a Directive on the appointment of legal representatives.The e-evidence package was accompanied by an impact assessment.This assessment asserts that e-evidence is requested in half of all investigations (first premise), that the mutual legal assistance treaties (MLAT) system is an inefficient channel for that purpose (second premise), and that as a result, two thirds of crimes cannot be effectively investigated (third premise).I challenge the empirical soundness of these three findings and argue that the percentages and figures used frame the problem fundamentally on technical and efficiency grounds.There is no reference to the political and economic motivations behind the promotion of a policy shift from MLAT to direct cooperation, which in my view, is the fourth and lost premise.
POSITIVE EMOTIONS AND EMPLOYEES’ PROTECTION-MOTIVATED BEHAVIOURS: A MODERATED MEDIATION MODEL

This study explores the relationship between positive emotions and protection-motivated behaviours by focusing on the mediating role of self-efficacy and the moderating role of information security awareness. Based on a sample of 215 full-time employees from various organizations in China, the results of hierarchical regression and moderated path analysis indicate that positive emotions positively influence protection-motivated behaviours, and self-efficacy partially mediates this relationship. In addition, information security awareness has a positive moderating effect on the relationships between positive emotions and self-efficacy and between self-efficacy and protectionmotivated behaviours. Furthermore, the findings show that information security awareness has a positive moderating effect on the mediating effect of self-efficacy between positive emotions and protection-motivated behaviours. The theoretical and practical implications of these results, as well as directions for future research, are discussed.
Coronamask: A Face Mask Detector for Real-Time Data

COVID -19 (2019 novel coronavirus) which started in China had spread all over the world rapidly.It is the worst health crisis the whole world has suffered after World War II.Many precautionary measures have been indicated by the World Health Organisation (WHO) like to maintain social distancing, wear masks, wash hands with soap for 20 seconds and many more.Wearing masks in public places is quite an effective measure to stay protected from this pandemic.There is very few research done for detecting face masks.This paper contributes to the welfare of human beings and proposes CoronaMask, a highly effective face mask detector.The proposed model uses the deep learning convolutional neural network (CNN) algorithm as a base for detecting faces.In this study, the dataset has been created which consists of 1238 images which are divided into two classes as "mask" and "no_mask".This model also takes live streaming videos as input and detects faces which are wearing masks and which are not wearing a mask.The convolutional neural network is trained on the dataset and it gives 95% of accuracy.CoronaMask, a two-phase face mask detector works in identifying masks in images and also in real-time video streams.
D-Talk: Sign Language Recognition System for People with Disability using Machine Learning and Image Processing

Communication plays a significant role in making the world a better place.Communication creates bonding and relations among the people, whether persona, social, or political views.Most people communicate efficiently without any issues, but many cannot due to disability.They cannot hear or speak, which makes Earth a problematic place to live for them.Even simple basic tasks become difficult for them.Disability is an emotive human condition.It limits the individual to a certain level of performance.Being deaf and dumb pushes the subject to oblivion, highly introverted.In a world of inequality, this society needs empowerment.Harnessing technology to improve their welfare is necessary.In a tech era, no one should be limited due to his or her inability.The application of technology should create a platform or a world of equality despite the natural state of humans.On the other hand, technology is the most innovative thing on Earth for every time the clock ticks, researchers, software engineers, programmers, and information technology specialists are always coming up with bright ideas to provide convenience to everyone.This paper shows how artificial intelligence is being used to help people who are unable to do what most people do in their everyday lives.Aligned with communication, D-talk is a system that allows people who are unable to talk and hear be fully understood and for them to learn their language easier and also for the people that would interact and communicate with them.This system provides detailed hand gestures that show the interpretation at the bottom so that everyone can understand them.This research allows the readers to learn the system and what it can do to people who are struggling with what they are not capable of and will provide the technical terms on how the system works.
Institutional Quality, Financial Development and Inclusive Growth: Asymmetric Cointegration Approach

The study examined the nexus between institutional quality, financial development and inclusive growth in Nigeria for the period 1984-2017.The study employed asymmetric cointegration approach to study the long-run relationship of institutional quality, financial development and inclusive growth in Nigeria.The results of the study showed that there is a long-run relationship between institutional quality, financial development and inclusive growth in Nigeria.It was also found that adjustments process to equilibrium for institutional quality, financial development and inclusive growth were asymmetric in Nigeria.The study therefore concludes that institutional quality and financial development are crucial variables that influence inclusive growth in Nigeria.
Low power pseudo-random number generator based on lemniscate chaotic map

Lemniscate chaotic map (LCM) provides a wide range of control parameters, canceling the need for several rounds of substitutions, and excellent performance in the confusion process. Unfortunately, the hardware model of LCM is complex and consumes high power. This paper presents a proposed low power hardware model of LCM called practical lemniscate chaotic map (P-LCM) depending on trigonometric identities to reduce the complexity of the conventional model. The hardware model designed and implement into the field programmable gate array (FPGA) board, Spartan-6 SLX45FGG484-3. The proposed model achieves a 48.3 % reduction in used resources and a 34.6 % reduction in power consumption compared to the conventional LCM. We also introduce a new pseudo-random number generator based on a proposed low power P-LCM model and perform the randomization tests for the proposed encryption system.
Encrypted Traffic Classification Based on Unsupervised Learning in Cellular Radio Access Networks

Traffic classification will be a key aspect in the operation of future 5G cellular networks, where services of very different nature will coexist. Unfortunately, data encryption makes this task very difficult. To overcome this issue, flow-based schemes have been proposed based on payload-independent features extracted from the Internet Protocol (IP) traffic flow. However, such an approach relies on the use of expensive traffic probes in the core network. Alternatively, in this paper, an offline method for encrypted traffic classification in the radio interface is presented. The method divides connections per service class by analyzing only features in radio connection traces collected by base stations. For this purpose, it relies on unsupervised learning, namely agglomerative hierarchical clustering. Thus, it can be applied in the absence of labeled data (seldom available in operational cellular networks). Likewise, it can also identify new services launched in the network. Method assessment is performed over a real trace dataset taken from a live Long Term Evolution (LTE) network. Results show that traffic shares per application class estimated by the proposed method are similar to those provided by a vendor report.
Stamps extraction using local adaptive k- means and ISODATA algorithms

&lt;span&gt;One of the main difficulties facing the certified documents documentary archiving system is checking the stamps system, but, that stamps may be contains complex background and surrounded by unwanted data. Therefore, the main objective of this paper is to isolate background and to remove noise that may be surrounded stamp. Our proposed method comprises of four phases, firstly, we apply k-means algorithm for clustering stamp image into a number of clusters and merged them using ISODATA algorithm. Secondly, we compute mean and standard deviation for each remaining cluster to isolate background cluster from stamp cluster. Thirdly, a region growing algorithm is applied to segment the image and then choosing the connected region to produce a binary mask for the stamp area. Finally, the binary mask is combined with the original image to extract the stamp regions. The results indicate that the number of clusters can be determined dynamically and the largest cluster that has minimum standard deviation (i.e., always the largest cluster is the background cluster). Also, show that the binary mask can be established from more than one segment to cover are all stamp’s disconnected pieces and it can be useful to remove the noise appear with stamp region.&lt;/span&gt;
Utilizing Cooperative Integrated Reading and Composition (CIRC) with mobile Learning to Enhance Students' Reading Comprehension

This present study aims to find out the how significant the utilization of Cooperative Integrated Reading and Composition (CIRC) with mobile learning on students’ reading comprehension. This study is conducted by using the quantitative method with pre-experimental design. The instrument used in in this study was test sheet called as Reading Comprehension Test (RCT), and it employed in pre-test and post-test, it was developed and selected from student textbooks, story books, and authentic materials to measure students’ reading comprehension achievement. The present results reveal that CIRC teaching strategy with mobile learning (m-learning) improved students’ performance in reading comprehension, it was indicated from the result of paired samples test (t-test) that the value of the t-observed significant is at .000 is lower than .050 (.000 &lt; .050, this means students’ improvement is significant after exposed by the experimentation (treatment) process. Based on the result, it concluded that the utilization of Cooperative Integrated Reading and Composition (CIRC) with mobile learning as teaching strategy can enhance students’ reading comprehension.Keywords: Cooperative Integrated Reading and Composition (CIRC), Mobile Learning, Reading Comprehension.
Virtual chain: A storage model supporting cross‐blockchain transaction

Summary At present, blockchain is not only applied in the financial field, but also extended to the medical, insurance, internet of things, and many other fields. Its independence leads to more and more significant problems of network isolation. To address the problem of the cooperative operation between different blockchains, this article proposes virtual chain, a storage model supporting cross‐blockchain transaction. It not only ensures that cross‐chain transactions are tamper‐proof and traceable but also realizes completely decentralized storage. First, to tackle the multidimensional heterogeneous problem existing in cross‐chain trading, a data structure based on prefix tree is proposed to manage cross‐chain trading data on top of source chain and target chain. Second, a signature verification mechanism based on elliptic curve is proposed to meet the operational atomicity requirements of both parties of cross‐chain transaction. Third, the data fusion technology is used to expand the block capacity indirectly to achieve the high throughput of cross‐chain transactions. Finally, by using the credibility‐based access control and block chain technology to ensure the security of the data storage model, and conduct security analysis from three aspects of tamper‐proof, data protection and anti‐conspiracy node attack. The experiment shows that the proposed cross‐chain transaction storage model can realize the efficient storage of the heterogeneous data, and satisfy the high requirement of the cross‐chain transaction. Moreover, the virtual chain storage model establishes a safe and reliable working environment.
Teaching the critical role of designers in the data society: the DensityDesign approach

In the modern 'data society', designers play a key role in the creation of artefacts that mediate our access to data and information. These artefacts include data visualisations and interfaces. Within this context, there is a growing risk of design educators training professionals who are indifferent to, or unaware of, the political power of the devices they contribute to creating. In this paper, we draw on our experiences in the DensityDesign course to identify and formalise a didactical approach providing students with opportunities to critically reflect on their work while gaining the technical skills they need as information designers. The paper describes the course’s historical evolution, its didactical goals and its current structure. It then provides an overview of the didactical approach identifying practices that other design instructors can reproduce, entirely or partially, at three different levels: through the methodological framework, the situational tactics, and the research artefacts students produce throughout the course. Finally, a critical discussion evaluating the limits and risks of the proposed approach is provided based on our didactical experiences.