Reviewerly/exHarmony huggingface dataset:
A corpus analysis of online news comments using the Appraisal framework

We present detailed analyses of the distribution of Appraisal categories (Martin and White, 2005) in a corpus of online news comments. The corpus consists of just over one thousand comments posted in response to a variety of opinion pieces on the website of the Canadian newspaper The Globe and Mail. We annotated all the comments with labels corresponding to different categories of the Appraisal framework. Analyses of the annotations show that comments are overwhelmingly negative, and that they favour two of the subtypes of Attitude (Judgment and Appreciation) over the third, Affect. The paper contributes a methodology for annotating Appraisal, and results that show the interaction of Appraisal with negation, the constructive (or not) nature of comments, and the level of toxicity found in them. The results show that highly opinionated language is expressed as an objective opinion (Judgement and Appreciation) rather than an emotional reaction (Affect). This finding, together with the interplay of evaluative language with constructiveness and toxicity in the comments, can be applied to the automatic moderation of comments.
The effect of renewal privacy policy whatsapp to customer behavior

Changes in WhatsAap policy cause confusion and tend to be anxious for users where each user is required to accept the WhatsAap management policy in order to link information on whatsAap with Facebook no later than February 8, 2021. With the number of users reaching millions of people this certainly affects decisions that are not easy. This study uses a qualitative method by conducting non-structured interviews with 10 informants who are the admin of the WhatsApp group which has a group of at least 100 people. Because the identity of the informant must be kept secret, the authors provide the code R1-R10. From the research results, it was found that consumers were aware of this Whatsapp policy and regretted this policy and would move to other safer and more comfortable applications
Universal Models and Platforms in E-Learning

The study describes the effectiveness of digital technological solutions in education. Students, postgraduates, teachers, and medical business representatives have tested a comprehensive educational product based on the capabilities of learning management system (LMS) platforms. The online course was implemented on the e-learning platforms such as Moodle, Schoology, TalentLMS, Canvas LMS, Docebo, iSpringLearn's, Brightspace, Absorb LMS. The implementation of advanced e-learning models ensured the effectiveness of the educational process. The focus on the development of the personal potential of each educational process participant made it possible to create a personalized learning environment and evoke a sense of personal significance and importance. The focus on feedback has created a creative communication environment for the exchange of knowledge, thoughts and ideas satisfying participants' educational needs for self-expression and creativity. The collective creativity of the educational process participants made it possible to develop a high-quality corporate culture of the students involved, which became the basis for establishing business partnerships in the system of education, science, and business. Based on the observations, a universal model of an effective educational process in the digital age has been developed.
Infrared Image Deblurring Based on Generative Adversarial Networks

Blind deblurring of a single infrared image is a challenging computer vision problem. Because the blur is not only caused by the motion of different objects but also by the relative motion and jitter of cameras, there is a change of scene depth. In this work, a method based on the GAN and channel prior discrimination is proposed for infrared image deblurring. Different from the previous work, we combine the traditional blind deblurring method and the blind deblurring method based on the learning method, and uniform and nonuniform blurred images are considered, respectively. By training the proposed model on different datasets, it is proved that the proposed method achieves competitive performance in terms of deblurring quality (objective and subjective).
Sit Here: Placing Virtual Machines Securely in Cloud Environments

A Cloud Computing Environment (CCE) leverages the advantages offered by virtualisation to enable virtual machines (VMs) within the same physical machine (PM) to share physical resources.Cloud service providers (CSPs) accommodate the fluctuating resource demands of cloud users dynamically, through elastic resource provisioning.CSPs use VM allocation techniques such as VM placement and VM migration to optimise the use of shared physical resources in the CCE.However, these techniques are exposed to potential security threats that can lead to the problem of malicious co-residency between VMs.This threat happens when a malicious VM is co-located with a critical (or target) VM on the same PM.Hence, the VM allocation techniques need to be made secure.While earlier works propose specific solutions to address this malicious co-residency problem, our work here proposes to investigate the allocation patterns that are more likely to lead to a secure allocation.Furthermore, we introduce a security-aware VM allocation algorithm (SRS) that aims to allocate the VMs securely, to reduce the potential for co-residency between malicious and target VMs.Our study shows: (i) our SRS algorithm outperforms all state-of-the-art allocation algorithms and (ii) algorithms that adopt stacking-based behaviours are more likely to return secure allocations than those with spreading or random behaviours.
Survey on the Loss Function of Deep Learning in Face Recognition

With the continuous development of face recognition network, the selection of loss function plays an increasingly important role in improving accuracy. The loss function of face recognition network needs to minimize the intra-class distance while expanding the inter-class distance. So far, one of our mainstream loss function optimization methods is to add penalty terms, such as orthogonal loss, to further constrain the original loss function. The other is to optimize using the loss based on angular/cosine margin. The last is Triplet loss and a new type of joint optimization based on HST Loss and ACT Loss. In this paper, based on the three methods with good practical performance and the joint optimization method, various loss functions are thoroughly reviewed.
Transductive Transfer Learning for Domain Adaptation in Brain Magnetic Resonance Image Segmentation

Segmentation of brain images from Magnetic Resonance Images (MRI) is an indispensable step in clinical practice. Morphological changes of sub-cortical brain structures and quantification of brain lesions are considered biomarkers of neurological and neurodegenerative disorders and used for diagnosis, treatment planning, and monitoring disease progression. In recent years, deep learning methods showed an outstanding performance in medical image segmentation. However, these methods suffer from generalisability problem due to inter-centre and inter-scanner variabilities of the MRI images. The main objective of the study is to develop an automated deep learning segmentation approach that is accurate and robust to the variabilities in scanner and acquisition protocols. In this paper, we propose a transductive transfer learning approach for domain adaptation to reduce the domain-shift effect in brain MRI segmentation. The transductive scenario assumes that there are sets of images from two different domains: (1) source—images with manually annotated labels; and (2) target—images without expert annotations. Then, the network is jointly optimised integrating both source and target images into the transductive training process to segment the regions of interest and to minimise the domain-shift effect. We proposed to use a histogram loss in the feature level to carry out the latter optimisation problem. In order to demonstrate the benefit of the proposed approach, the method has been tested in two different brain MRI image segmentation problems using multi-centre and multi-scanner databases for: (1) sub-cortical brain structure segmentation; and (2) white matter hyperintensities segmentation. The experiments showed that the segmentation performance of a pre-trained model could be significantly improved by up to 10%. For the first segmentation problem it was possible to achieve a maximum improvement from 0.680 to 0.799 in average Dice Similarity Coefficient (DSC) metric and for the second problem the average DSC improved from 0.504 to 0.602. Moreover, the improvements after domain adaptation were on par or showed better performance compared to the commonly used traditional unsupervised segmentation methods (FIRST and LST), also achieving faster execution time. Taking this into account, this work presents one more step toward the practical implementation of deep learning algorithms into the clinical routine.
Variable-length codes independent or closed with respect to edit relations

We investigate inference of variable-length codes in other domains of computer science, such as noisy information transmission or information retrieval-storage: in such topics, traditionally mostly constant-length codewords act. The study is relied upon the two concepts of independent and closed sets: given an alphabet A and a binary relation τ⊆A⁎×A⁎, a set X⊆A⁎ is τ-independent if τ(X)∩X=∅; X is τ-closed if τ(X)⊆X. We focus to those word relations whose images are computed by applying some peculiar combinations of deletion, insertion, or substitution. In particular, characterizations of variable-length codes that are maximal in the families of τ-independent or τ-closed codes are provided.
Birds Identification System using Deep Learning

Identifying birds is one of challenging role for bird watchers due to the similarity of the birds' forms/image background and the lack of experience for watchers.So, it needs a computer system based images to help birdwatchers in order to identify birds.This study aims at investigating the use of deep learning for birds' identification using convolutional neural network for extracting features from images.The investigation was performed on database contained 4340 images that collected by the paper author from Jordan.The Principal Component Analysis (was applied on layer 6 and 7, as well as on the statistical operations of merging the two layers like: average, minimum, maximum and combine of both layers.The datasets were investigated by the following classifiers: Artificial neural networks, K-Nearest Neighbor, Random Forest, Naïve Bayes and Decision Tree.Whereas, the metrics used in each classifier are: accuracy, precision, recall, and F-Measure.The results of investigation include and not limited to the following, the PCA used on the deep features does not only reduce the dimensionality, and therefore, the training/testing time is reduced significantly, but also allows for increasing the identification accuracy, particularly when using the Artificial Neural Networks classifier.Based on the results of classifiers; Artificial neural networks showed high classification accuracy (70.9908), precision (0.718), recall (0.71) and F-Measure (0.708) compared to other classifiers.
A Generative Model based Adversarial Security of Deep Learning and Linear Classifier Models

In recent years, machine learning algorithms have been applied widely in various fields such as health, transportation, and the autonomous car. With the rapid developments of deep learning techniques, it is critical to take the security concern into account for the application of the algorithms. While machine learning offers significant advantages in terms of the application of algorithms, the issue of security is ignored. Since it has many applications in the real world, security is a vital part of the algorithms. In this paper, we have proposed a mitigation method for adversarial attacks against machine learning models with an autoencoder model that is one of the generative ones. The main idea behind adversarial attacks against machine learning models is to produce erroneous results by manipulating trained models. We have also presented the performance of autoencoder models to various attack methods from deep neural networks to traditional algorithms by using different methods such as non-targeted and targeted attacks to multi-class logistic regression, a fast gradient sign method, a targeted fast gradient sign method and a basic iterative method attack to neural networks for the MNIST dataset.
THE EFFECTS OF CULTURAL BASED TEXT TYPES IN READING COMPREHENSION

Background and Purpose: This research aims to investigate the effects of Malay, Chinese and Indian cultural based text types on reading comprehension.&#x0D; &#x0D; Methodology: An exploratory case study research design was employed to explore the use of cultural schemata in reading comprehension of three types of cultural text format: narrative, descriptive and info graphic. The purposive sampling method was used to select participants based on their ethnicities, English proficiency level, and their scores in prior knowledge assessment and retelling assessment. A total of 15 students, between 11- 12 years old, were selected. The retelling technique in written form, a comprehension test and an interview protocol were used as instruments to gather data in this research and they were analysed both quantitatively and qualitatively.&#x0D; &#x0D; Findings: The overall findings have shown that the comprehension test scores showed better performance in info graphic texts. However, the retelling assessment scores showed better performance in retelling narrative texts. The participants’ main reason for text incomprehensibility was due to the unfamiliar cultural content regardless of the types of text format&#x0D; .&#x0D; Contributions: This study aids teachers in their pedagogical decisions when selecting and adapting cultural texts for reading comprehension&#x0D; &#x0D; Keywords: Comprehension test, cultural schemata, reading comprehension, retelling assessment, text types.&#x0D; Cite as: Thirunavukarasu, I., &amp; Raja Harun, R. N. S. (2021). The effects of cultural based text types in reading comprehension. Journal of Nusantara Studies, 6(1), 1-23. http://dx.doi.org/10.24200/jonus.vol6iss1pp1-23
Analysis and Prediction of “AI + Education” Attention Based on Baidu Index—Taking Guizhou Province as an Example

Studying the attention of “artificial intelligence + education” in ethnic areas is of great significance for China for promoting the integrated development of new educational modes and modern technology in the western region. Guizhou province is an area inhabited by ethnic minorities, located in the heart of Southwest China. The development of its intelligent education has strong enlightenment for the whole country and the region. Therefore, this paper selects the Baidu Index of “artificial intelligence (AI) + education” in Guizhou province from 2013 to 2020, analyzes the spatial–temporal characteristics of its network attention by using the elastic coefficient method, and builds the ARIMA model on this basis to predict future development. The results show that the public’s attention to “AI + education” differs significantly in time and space. Then, according to the prediction results, this paper puts forward relevant suggestions for the country to promote the sustainable development of education in western ethnic areas.
The Implementation of Duolingo Mobile Application in English Vocabulary Learning

The purposes of this research are as follows: to obtain data and to analyze the effects of the implementation of Duolingo mobile application in English vocabulary. The research was held at SDN 02 Ciputat. All data variables were taken through tests. For vocabulary mastery tests, the data was taken in the questionnaires form. The method used was to conduct a system quality test (Software Quality Assurance) of software quality based on the factors and the criteria established. The data shows that the application gets a score with a pretty good predicate (73,8%), so learning media using the Duolingo mobile application can be applied especially for students in understanding English.&lt;p class="Author"&gt; &lt;/p&gt;
A Resource Recommendation Algorithm for Online English Learning Systems Based on Learning Ability Evaluation

Mature online English learning platforms should provide students with necessary learning resources, ensure efficient access to learning projects, and offer the optimal learning experience. However, the traditional recommendation methods for English learning resources cannot satisfy the in-depth learning demand of students. To solve the problem, this paper designs a resource recommendation algorithm for online English learning systems based on learning ability evaluation. Firstly, the workflow of the designed algorithm was introduced, and a four-layer test system was developed for students’ English learning ability evaluation. Next, an English learning ability evaluation method was proposed based on the maximum expectation algorithm, as well as the estimation methods for parameters like learning ability, degree of discrimination, difficulty, and guess coefficient. Experimental results demonstrated the good effect of the proposed resource recommendation algorithm. The research findings provide a reference for resource recommendation of other online learning systems.
Monetary Dynamics With Proof of Stake

In recent years blockchain consensus mechanisms based on Proof of Stake gained increasing attention as an alternative to Proof of Work, which requires high energy consumption. In its original version Proof of Stake hinges on the idea that, for a user, the likelihood to confirm the next block is positively related to the amount of currency units held in the wallet, and possibly also on the time length which the money has been unspent for. In a simple framework with risk neutral users we provide some early insights on the monetary equilibrium of Proof of Stake based platforms. In particular, we find that the aggregate demand and supply of currency may not coincide, which implies that users could hold suboptimal quantities of the currency. Furthermore, we also discuss how symmetric stationary states of the system could be implausible. As a consequence, a long run uniform distribution of money would seem unlikely unless appropriate measures are introduced.
Understanding Natural Disaster Scenes from Mobile Images Using Deep Learning

With the ubiquitous use of mobile imaging devices, the collection of perishable disaster-scene data has become unprecedentedly easy. However, computing methods are unable to understand these images with significant complexity and uncertainties. In this paper, the authors investigate the problem of disaster-scene understanding through a deep-learning approach. Two attributes of images are concerned, including hazard types and damage levels. Three deep-learning models are trained, and their performance is assessed. Specifically, the best model for hazard-type prediction has an overall accuracy (OA) of 90.1%, and the best damage-level classification model has an explainable OA of 62.6%, upon which both models adopt the Faster R-CNN architecture with a ResNet50 network as a feature extractor. It is concluded that hazard types are more identifiable than damage levels in disaster-scene images. Insights are revealed, including that damage-level recognition suffers more from inter- and intra-class variations, and the treatment of hazard-agnostic damage leveling further contributes to the underlying uncertainties.
The Evaluation of User Experience Testing for Retrieval-based Model and Deep Learning Conversational Agent

The use of a conversational agent to relay information on behalf of individuals has gained worldwide acceptance.The conversational agent in this study was developed using Retrieval-based Model and Deep Learning to enhance the user experience.Nevertheless, the successfulness of the conversational agent could only be determined upon the evaluation.Thus, the testing was performed in the quantitative approach via questionnaire survey to capture user experience upon the usage of the conversational agent in terms of Usability, Usefulness and Satisfaction.The questionnaire survey was tested via statistical tool for reliability and validation test and proven to be carried out.The test results indicate positive experience towards the usage of the conversational agent and the outcome of the testing showed promising results and proof the success of this study, with immense contributions to the field of conversational agent.
Prediction of Sea Surface Current Velocity and Direction Using LSTM

Labuan Bajo is considered to have an important role as a transportation route for traders and tourists. Therefore, it is necessary to have a further understanding of the condition of the waters in Labuan Bajo, one of them is sea currents. The purpose of this research is to predict sea surface flow velocity and direction using LSTM. There are many prediction methods, one of them is Long short-term memory (LSTM). The fundamental of LSTM is to process information from the previous memory by going through three gates, that is forget gate, input gate, and output gate so the output will be the input in the next process. Based on trials with several parameters namely Hidden Layer, Learning Rate, Batch Size, and Learning rate drop period, it achieved the smallest MAPE values of U and V components of 14.15% and 8.43% with 50 hidden layers, 32 Batch size and 150 Learn rate drop.
Development and Evaluation of Speech Synthesis System Based on Deep Learning Models

This study concentrates on the investigation, development, and evaluation of Text-to-Speech Synthesis systems based on Deep Learning models for the Azerbaijani Language. We have selected and compared state-of-the-art models-Tacotron and Deep Convolutional Text-to-Speech (DC TTS) systems to achieve the most optimal model. Both systems were trained on the 24 h speech dataset of the Azerbaijani language collected and processed from the news website. To analyze the quality and intelligibility of the speech signals produced by two systems, 34 listeners participated in an online survey containing subjective evaluation tests. The results of the study indicated that according to the Mean Opinion Score, Tacotron demonstrated better results for the In-Vocabulary words; however, DC TTS indicated a higher performance of the Out-Of-Vocabulary words synthesis.
Information Hiding Using Minification

Information hiding techniques have been recently getting increased attention from the security community. This is because attackers often apply a variety of data hiding methods to exfiltrate confidential information, enable covert transfers between the compromised victim's machine and an attacker-operated infrastructure, or stealthily transmit additional malicious tools. Furthermore, such data concealment can be realized using different types of carriers, for instance, digital images, video, audio, text, or network traffic. Therefore, by carefully inspecting various data hiding methods, it is possible to assess the implications that such threats cause and to evaluate the preparedness of the existing defensive solutions. Minification is a popular mean for source code size reduction while preserving its complete functionality. In effect, the data transfer can be realized in a more efficient manner. Considering the above, in this paper, we systematically evaluate if the minification process can be effectively used for secret data transfer. The performed extensive experimental evaluation and obtained results indicate that the threat is real, thus countermeasures need to be adjusted accordingly.