Reviewerly/exHarmony huggingface dataset:
Effect of Age on Information Security Awareness Level among Young Internet Users in Malaysia

The negligence to take proper security precautions while using the Internet has been lacking nowadays, especially among youth.Although youth deal the most with smart gadgets, past research has found that there is still lack of information security awareness among them.This can be shown by the number of cybercrimes arising day by day such as, cyberbullying, phishing, hacking, online threats, virus attacks, and so on.Thus, research has been conducted using quantitative methods by utilising a questionnaire to measure the information security awareness level among youth in Malaysia.The aim of this study is to determine the effect of age on the information security awareness level among young Internet users in Malaysia.400 respondents have been participated in this survey.The findings of this study reveals that there is strong significant relationship between age (18-20 years old; 21-30 years old; 31-40 years old) and information security awareness (password usage, data protection, and data backup).Besides, there is no significant relationship between age and personal data sharing.However, respondents from 21-30 years old showed more concern towards information security awareness in this study.It is highly recommended for young Internet users to identify online threats and be aware of the security breaches encountered.
Students’ competencies discovery and assessment using learning analytics and semantic web

Traditional content-based assessment systems, which depend on the score as a key criterion for students’ evaluation, have proven to have many drawbacks, especially with the development of learning methods in recent years. Based on these developments, there is a need to adopt new assessment methods to assess the actual skills of students in the digital age. Therefore, the competency-based assessment approach is adopted in this paper to address the subject of students’ competency modelling and discovery in technology-enhanced learning systems. This method of assessment is perfectly suited to modern teaching trends. The authors proposed an approach of assessment semantic analytics to be used for discovering and assessing students’ competencies. This study notes that, all knowledge about students and their competencies has been modelled by semantic representations. Student’s models have been subjected to a set of learning analytics approaches to analyse data generated by students’ activities in order to discover their explicit and latent competencies hidden behind their activities. This experimental study indicates that the competency-based assessment approach is efficient and expected to show significant advantages in evaluating students’ competencies. Implications for practice or policy: Students become able to organise their gains, then integrate and employ them in solving life's problems. Educators get to know more about the extent to which the objectives of their educational process are achieved by evaluating the intellectual, cultural, knowledge, and skilful assets that the learners obtain. Educational policymakers can have a pedagogical and technical vision to move from the culture of content-based evaluations to a competency-based assessment.
Farming Assistant Web Services: Agricultor

Abstract: Our farming assistant web services provides assistance to new as well as establish farmers to get the solutions to dayto-day problems faced in the field. A farmer gets to connect with other farmers throughout India to get more information about a particular crop which is popular in other states. Keywords: Farmers, Assistance, Web Development
The Identifiability of Copula Models for Dependent Competing Risks Data With Exponentially Distributed Margins

In this paper, we prove the identifiability property of Archimedean copula models for dependent competing risks data when at least one of the failure times is exponentially distributed.With this property, it becomes possible to quantify the dependence between competing events based on exponentially distributed dependent censored data.We demonstrate our estimation procedure using simulation studies and illustrate the estimation procedure by applying it to a survival data.
Secretarial work in pandemic times: new skills or old tasks?

This paper aimed at raising the perception of secretaries about interferences or redimensions in the usage of their skills, due to the remote work modality, imposed by the Covid-19 pandemic. A qualitative research was carried out through semi-structured interviews with eight executive secretaries working in the private sector. Among the main results obtained, it was found that most of the interviewees noticed changes in the usage of their skills, due to the varied changes in their work scopes, as well as the new environment, providing them more autonomy and freedom. In the post-pandemic context, most respondents confirmed that the companies they work for, will not return to work in the same way and perspective as it was before.
Aerial Image Enhancement based on YCbCr Color Space

Enhancing aerial images plays an important role in many remote sensing applications; aerial images are often affected by dust, smoke, and fog.In this study, a new method was proposed to enhance aerial images.The proposed method is enhanced the light value (Y) in the YCbCr color transform by using the Sigmoid function mapping and Adaptive Histogram Equalization techniques, While the chromatic compounds (CbCr) are enhanced by Dark Channel Prior (DCP).To know the efficiency of enhancement, the no-reference quality measures as Entropy and Average Gradient (AG) have been calculated, which are used to see the quality of the proposed method in comparison with Image Entropy and Information Fidelity (IEIF), Multiscale Retinex with color restoration (MSRCR), Color Enhancement Scene Estimation (CESE), traditional (DCP), and Histogram Equalization (HE).From the results, it is found that the proposed method has succeeded in enhancing the aerial images better than other methods.Where it had high values for the rate for Entropy (7.73) and AG (68.75).
Obtaining Calibrated Probabilities with Personalized Ranking Models

For personalized ranking models, the well-calibrated probability of an item being preferred by a user has great practical value. While existing work shows promising results in image classification, probability calibration has not been much explored for personalized ranking. In this paper, we aim to estimate the calibrated probability of how likely a user will prefer an item. We investigate various parametric distributions and propose two parametric calibration methods, namely Gaussian calibration and Gamma calibration. Each proposed method can be seen as a post-processing function that maps the ranking scores of pre-trained models to well-calibrated preference probabilities, without affecting the recommendation performance. We also design the unbiased empirical risk minimization framework that guides the calibration methods to learning of true preference probability from the biased user-item interaction dataset. Extensive evaluations with various personalized ranking models on real-world datasets show that both the proposed calibration methods and the unbiased empirical risk minimization significantly improve the calibration performance.
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model Compression

Pre-trained Language Models (PLMs) have achieved great success in various Natural Language Processing (NLP) tasks under the pre-training and fine-tuning paradigm. With large quantities of parameters, PLMs are computation-intensive and resource-hungry. Hence, model pruning has been introduced to compress large-scale PLMs. However, most prior approaches only consider task-specific knowledge towards downstream tasks, but ignore the essential task-agnostic knowledge during pruning, which may cause catastrophic forgetting problem and lead to poor generalization ability. To maintain both task-agnostic and task-specific knowledge in our pruned model, we propose ContrAstive Pruning (CAP) under the paradigm of pre-training and fine-tuning. It is designed as a general framework, compatible with both structured and unstructured pruning. Unified in contrastive learn- ing, CAP enables the pruned model to learn from the pre-trained model for task-agnostic knowledge, and fine-tuned model for task-specific knowledge. Besides, to better retain the performance of the pruned model, the snapshots (i.e., the intermediate models at each pruning iteration) also serve as effective supervisions for pruning. Our extensive experiments show that adopting CAP consistently yields significant improvements, especially in extremely high sparsity scenarios. With only 3% model parameters reserved (i.e., 97% sparsity), CAP successfully achieves 99.2% and 96.3% of the original BERT performance in QQP and MNLI tasks. In addition, our probing experiments demonstrate that the model pruned by CAP tends to achieve better generalization ability.
From Good to Best: Two-Stage Training for Cross-Lingual Machine Reading Comprehension

Cross-lingual Machine Reading Comprehension (xMRC) is a challenging task due to the lack of training data in low-resource languages. Recent approaches use training data only in a resource-rich language (such as English) to fine-tune large-scale cross-lingual pre-trained language models, which transfer knowledge from resource-rich languages (source) to low-resource languages (target). Due to the big difference between languages, the model fine-tuned only by the source language may not perform well for target languages. In our study, we make an interesting observation that while the top 1 result predicted by the previous approaches may often fail to hit the ground-truth answer, there are still good chances for the correct answer to be contained in the set of top k predicted results. Intuitively, the previous approaches have empowered the model certain level of capability to roughly distinguish good answers from bad ones. However, without sufficient training data, it is not powerful enough to capture the nuances between the accurate answer and those approximate ones. Based on this observation, we develop a two-stage approach to enhance the model performance. The first stage targets at recall; we design a hard-learning (HL) algorithm to maximize the likelihood that the top k predictions contain the accurate answer. The second stage focuses on precision, where an answer-aware contrastive learning (AA-CL) mechanism is developed to learn the minute difference between the accurate answer and other candidates. Extensive experiments show that our model significantly outperforms strong baselines on two cross-lingual MRC benchmark datasets.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration

Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods need to iteratively optimize an objective function via a specific deformation model along with meticulous parameter tuning, but which have limited capabilities in registering images with large deformations. While deep learning-based methods can learn the complex mapping from input images to their respective deformation field, it is regression-based and is prone to be stuck at local minima, particularly when large deformations are involved. To this end, we present Stochastic Planner-Actor-Critic (spac), a novel reinforcement learning-based framework that performs step-wise registration. The key notion is warping a moving image successively by each time step to finally align to a fixed image. Considering that it is challenging to handle high dimensional continuous action and state spaces in the conventional reinforcement learning (RL) framework, we introduce a new concept `Plan' to the standard Actor-Critic model, which is of low dimension and can facilitate the actor to generate a tractable high dimensional action. The entire framework is based on unsupervised training and operates in an end-to-end manner. We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations. Our empirical results highlight that our work achieves consistent, significant gains and outperforms state-of-the-art methods.
MDPGT: Momentum-Based Decentralized Policy Gradient Tracking

We propose a novel policy gradient method for multi-agent reinforcement learning, which leverages two different variance-reduction techniques and does not require large batches over iterations. Specifically, we propose a momentum-based decentralized policy gradient tracking (MDPGT) where a new momentum-based variance reduction technique is used to approximate the local policy gradient surrogate with importance sampling, and an intermediate parameter is adopted to track two consecutive policy gradient surrogates. MDPGT provably achieves the best available sample complexity of O(N -1 e -3) for converging to an e-stationary point of the global average of N local performance functions (possibly nonconcave). This outperforms the state-of-the-art sample complexity in decentralized model-free reinforcement learning and when initialized with a single trajectory, the sample complexity matches those obtained by the existing decentralized policy gradient methods. We further validate the theoretical claim for the Gaussian policy function. When the required error tolerance e is small enough, MDPGT leads to a linear speed up, which has been previously established in decentralized stochastic optimization, but not for reinforcement learning. Lastly, we provide empirical results on a multi-agent reinforcement learning benchmark environment to support our theoretical findings.
The techno-startup course of the School of entrepreneurship as an introduction to innovation

The modern economy is in dire need of innovators who can design the technological image of the future. The best way to train future specialists in technonatural recognized as a project-based approach, augmented business education. But research has shown that in Russia, project activity in its existing forms does not fulfill its main functions: it does not give students the experience of creating innovations, does not develop critical thinking, does not form an interest in creating new products or services, their implementation and diffusion. The aim of the research was to search for approaches, methods and forms of training that will form students’ desire to choose a career in the field of technology innovation or high-tech entrepreneurship, give them the experience and knowledge necessary for the organization of a technology startup. In order to test the found pedagogical solutions, the techno-startup course of the school of entrepreneurship as an introduction to innovation. The study showed that the introduction of psychological training (teaching students the techniques of generating and testing ideas, empathy, conducting in-depth interviews, team building, planning, etc.), gamification of all types of classes, the development of course tasks as a tool for the development of design- and business thinking of students, monitoring the development of students ‘ competencies and components of their thinking with the help of specially prepared trackers, building the stages of the educational project in accordance with the stages of a real technology startup (the introduction of empathy stages, MVP, attracting external funding), allowed us to achieve the goal of the study.
Solving the interference problem for ellipses and ellipsoids: New formulae

The problem of detecting when two moving ellipses or ellipsoids overlap is of interest to robotics, CAD/CAM, computer animation, etc., where ellipses and ellipsoids are often used for modelling (and/or enclosing) the shape of the objects under consideration. By analysing symbolically the sign of the real roots of the characteristic polynomial of the pencil defined by two ellipses/ellipsoids A and B given by XTAX=0 and XTBX=0, we derive new formulae characterising when A and B overlap, are separate, or touch each other externally. This characterisation is defined by a minimal set of polynomial inequalities depending only on the entries of A and B so that we need only compute the characteristic polynomial of the pencil defined by A and B, det(TA+B), and not the intersection points between them. Compared with the best available approach dealing with this problem, the new formulae involve a smaller set of polynomials and less sign conditions. As an application, this characterisation provides also a new approach for exact collision detection of two moving ellipses or ellipsoids since the analysis of the univariate polynomials (depending on the time) in the previously mentioned formulae provides the collision events between them.
Discovering Lexical Similarity Using Articulatory Feature-Based Phonetic Edit Distance

Lexical Similarity (LS) between two languages uncovers many interesting linguistic insights such as genetic relationship, mutual intelligibility, and the usage of one's vocabulary into other.There are various methods through which LS is evaluated.In the same regard, this paper presents a method of Phonetic Edit Distance (PED) that uses a soft comparison of letters using the articulatory features associated with them.The system converts the words into the corresponding International Phonetic Alphabet (IPA), followed by the conversion of IPA into its set of articulatory features.Later, the lists of the set of articulatory features are compared using the proposed method.As an example, PED gives edit distance of German word vater (/faːtər/) and Persian word ‫'ﭘﺪﺭ'‬ ( pidar , /pedaer/) as 0.82; and similarly, Hebrew word ‫'שלום'‬ ( shalom , /ʃəlɒm/) and Arabic word ‫'ﺳﻼﻡ'‬ ( salam , /səla:m/) as 0.93, whereas for a juxtapose comparison, their IPA based edit distances are 4 and 2 respectively.Experiments are performed with six languages (Arabic, Hindi, Marathi, Persian, Sanskrit, and Urdu).In this regard, we extracted part of speech wise word-lists from the Universal Dependency corpora and evaluated the LS for every pair of language.Thus, with the proposed approach, we find the genetic affinity, similarity, and borrowing/loan-words despite having script differences and sound variation phenomena among these languages.
Cyber Forensic – A Literature Review

Cyber Forensics is a branch of forensic science that is aimed to restore, collect and examine the digital evidence of materials found in digital devices, in relation to cybercrimes. With the advancement in cyber area, frequent use of internet and technologies leads to cyber-attacks. Cyber forensic is opted for acquiring electronic information and investigation of malicious evidence found in system or on network in such a manner that makes it admissible in court. It is also used to recover lost information in a system. The retrived information is used to prosecute a criminal. Number of crimes committed against an internet and malware attacks over the digital devices have increased. This paper contains a brief review of the literature aimed to identify the relevant pieces of knowledge in the digital forensics field.
“Apacin” Learning Model in Writing Poetry for Elementary School

Students have difficulty understanding the learning material, which has an impact on students' low abilities. It will have an impact on low student learning outcomes. Many factors cause students difficulty in understanding the learning material. One of the influencing factors is the learning model. This research aims to produce a new learning model development called APACIN, an acronym for the learning syntax Observe, Think, Analyze, Create, and Information. The method used in this research is descriptive qualitative with a case study approach to elementary school students. The result of this study is that the APACIN learning model can provide alternative solutions to learning problems faced by teachers and students. Through this APACIN learning model, teachers and students can interact more, and students individually and in groups can provide solutions to contextual problems discussed in learning. In addition, the application of the APACIN learning model can build critical thinking, creative thinking, reflective thinking, and decision making in students. The APACIN learning model will make it easier for students to learn so that it will impact increasing student learning outcomes.
COMPARATIVE STUDY OF DISTANCE MEASURES ON FUZZY SUBTRACTIVE CLUSTERING

Clustering is a data analysis process which applied to classify the unlabeled data. Fuzzy clustering is a clustering method based on membership value which enclosing set of fuzzy as a measurement base for classification process. Fuzzy Subtractive Clustering (FSC) is included in one of fuzzy clustering method. This research applies Hamming distance and combined Minkowski Chebysev distance as a distance parameter in Fuzzy Subtractive Clustering. The objective of this research is to compare the output quality of the cluster from Fuzzy Subtractive Clustering by using Hamming distance and combine Minkowski Chebysev distance. The comparison of the two distances aims to see how well the clusters are produced from two different distances. The data used is data on hypertension. The variables used are age, gender, systolic pressure, diastolic pressure, and body weight. This research shows that the Partition Coefficient value resulted on Fuzzy Subtractive Clustering by applying combined Minkowski Chebysev distance is higher than the application of Hamming distance. Based on this, it can be concluded that in this study the quality of the cluster output using the combined Minkowski Chebysev distance is better.
Research on Teaching Practice of Blended Higher Education Based on Deep Learning Route

This paper establishes a hybrid education teaching practice quality evaluation system in colleges and constructs a hybrid teaching quality evaluation model based on a deep belief network. Karl Pearson correlation coefficient and root mean square error (RMSE) indicators are used to measure the closeness and fluctuation between the effective online teaching quality evaluation results evaluated by this method and the actual teaching quality results. The experimental results show the following: (1) As the number of iterations increases, the fitting error of the DBN model decreases significantly. When the number of iterations reaches 20, the fitting error of the DBN model stabilizes and decreases to below 0.01. The experimental results show that the model used in this method has good learning and training performance, and the fitting error is low. (2) The evaluation correlation coefficients are all greater than 0.85, and the root mean square error of the evaluation is less than 0.45, indicating that the evaluation results of this method are similar to the actual evaluation level and have small errors, which can be effectively applied to online teaching quality evaluation in colleges and universities.
Efficacy of Augmented Reality in Medical Education

Augmented reality has had a great development in various fields such as education, architecture, marketing, and tourism. However, there is another field in which it can be really useful, so much so that it can save lives: medicine. In this article, there is a detailed explanation of how augmented reality proved itself effective in medical education. The hospitals and health centers increasingly have more modern equipment that allows accurate diagnoses and effective treatments put up. This is the case of advances such as X-rays, magnetic resonances, ultrasound treatments, or computed tomography, among many others. The uses of augmented reality in medicine are very varied, although it is still a technology in an early stage of development. It is very likely that, in a short time, the applications of AR in the health sector will multiply. Moreover, it might be used in pandemic rather than typical or random criteria.