Reviewerly/exHarmony huggingface dataset:
Elements of unfairness in e-learning distance higher education in covid-19 era

Undoubtfully in covid-19 era one of the sectors seriously affected is that of education. More specifically, an issue widely discussed is that of the form of education offered to the interested parties under the new circumstances. Worldwide, at all levels of education (primary, secondary and tertiary), both educators and learners were forced by the exogenous factor of pandemic, not only to work at distance from their educational institution, but also to adjust in e-learning educational procedures in a very short time. Focusing on tertiary education, this rapid unavoidable change brought forward several educational issues and dysfunctions, as these are revealed from researchers’ publications. Though these issues and concerns are related to issues of unfairness, nevertheless this has not been pinpointed in literature. Thus, the aim of this paper is to identify which elements of unfairness are raised due to the constraints and dysfunctions occurred from the sudden change from face-to-face to online distance learning. For these purpose 15 related papers are critical reviewed. The results showed unfairness elements across countries, within countries, among universities, within universities and among students have been identified. More over two elements of unfairness have a horizontal impact in tertiary education since the beginning of the pandemic era. These unfairness issues highlighted seem that will engage the tertiary educational community and educational community in general, since distance education may become a dominant form of education in the near future. Thus, issues and concerns of fairness at distance education need to come to the proscenium.
Artificial intelligence and Canadian copyright: the quest for an author

As computers have developed, we have seen our relationship with them change and the computer’s role shift from that of a tool under the direct control of the artist to that of a “collaborator” or creative part-ner and, potentially, an autonomously creative entity [MccorMack, Bown, Dorin, MccaBe, Monro and whitelaw (2013)]. In the context of artificial intelligence, the grey area and most questions for now lie in the middle of the scale which blends human and algorithms participation. In particular, one may ask how does the Canadian copyright system protect (or not) creations made through such “collaboration”? We submit that part of the answer lies in the definition of “author”.
Permute Me Softly: Learning Soft Permutations for Graph Representations

Graph neural networks (GNNs) have recently emerged as a dominant paradigm for machine learning with graphs. Research on GNNs has mainly focused on the family of message passing neural networks (MPNNs). Similar to the Weisfeiler-Leman (WL) test of isomorphism, these models follow an iterative neighborhood aggregation procedure to update vertex representations, and they next compute graph representations by aggregating the representations of the vertices. Although very successful, MPNNs have been studied intensively in the past few years. Thus, there is a need for novel architectures which will allow research in the field to break away from MPNNs. In this paper, we propose a new graph neural network model, so-called <inline-formula><tex-math notation="LaTeX">$\pi$</tex-math></inline-formula>-GNN which learns a &#x201C;soft&#x201D; permutation (i.e., doubly stochastic) matrix for each graph, and thus projects all graphs into a common vector space. The learned matrices impose a &#x201C;soft&#x201D; ordering on the vertices of the input graphs, and based on this ordering, the adjacency matrices are mapped into vectors. These vectors can be fed into fully-connected or convolutional layers to deal with supervised learning tasks. In case of large graphs, to make the model more efficient in terms of running time and memory, we further relax the doubly stochastic matrices to row stochastic matrices. We empirically evaluate the model on graph classification and graph regression datasets and show that it achieves performance competitive with state-of-the-art models.
Multi-label classification approach for quranic verses labeling

Machine learning involves the task of training systems to be able to make decisions without being explicitly programmed. Important among machine learning tasks is classification involving the process of training machines to make predictions from predefined labels. Classification is broadly categorized into three distinct groups: single-label (SL), multi-class, and multi-label (ML) classification. This research work presents an application of a multi-label classification (MLC) technique in automating Quranic verses labeling. MLC has been gaining attention in recent years. This is due to the increasing amount of works based on real-world classification problems of multi-label data. In traditional classification problems, patterns are associated with a single-label from a set of disjoint labels. However, in MLC, an instance of data is associated with a set of labels. In this paper, three standard &lt;em&gt;MLC&lt;/em&gt; methods: &lt;span&gt;binary relevance (BR), classifier chain (CC), and label powerset (LP) algorithms are implemented with four baseline classifiers: support vector machine (SVM), naïve Bayes (NB), k-nearest neighbors (k-NN), and J48. The research methodology adopts the multi-label problem transformation (PT) approach. The results are validated using six conventional performance metrics. These include: hamming loss, accuracy, one error, micro-F1, macro-F1, and avg. precision. From the results, the classifiers effectively achieved above 70% accuracy mark. Overall, SVM achieved the best results with CC and LP algorithms.&lt;/span&gt;
AN ANALYSIS OF FLOUTING OF MAXIM PERFORMED BY POLITICIAN GUESTS IN MATA NAJWA TALK SHOW IN THE EPISODE OF ADU LANTANG JELANG PENENTUAN AND BABAK AKHIR PILPRES

In the process of communication, speakers are expected to obey the maxim of cooperative principles to convey messages effectively. However, many speakers disobey it for some reasons. Flouting of maxim is one of cases when speakers fail to observe the maxim to generate an implicature. This research aimed to find the types of flouting of maxim and the reasons of indirectness in flouting the maxim done by Politician guests in two episode of Mata Najwa Talk Show named Adu Lantang Jelang Penentuan and Babak Akhir Pilpres. The method of this research is descriptive research . The data of this research were the utterances of guests that flout the maxim of cooperative principles. It is found that there were total of 55 utterances that flout the maxim of cooperative principles that were analyzed. The results show that the Politician guests flout maxim of quantity and manner the most with frequency of 53.96% and 36.50%. Flouting of maxim of quality and relation rarely appeared with the same frequency, 4.76%. It is also found that the reasons of indirectness in flouting the maxim were vary such as interestingness, increasing the force’s of messages, competing goals, and politeness. However, increasing the force of the messages and politeness are two reasons that occurred more frequent in comparison with interestingness and competing goals. This research implicated that politician guests convey messages by flouting maxim for many reasons in order to gain support from masses.
FPGA Implementation of ECC Enabled Multi-factor Authentication (E-MFA) Protocol for IoT Based Applications

IoT platform creates attractive opportunities for our daily lives which make us smarter and more comfortable. IoT offers an incredible guarantee in the e-healthcare field by enhancing the quality of service with limited time-bound. The connectivity provided for e-healthcare devices poses overwhelming security and privacy concerns in this area. In this work, the Elliptic Curve Cryptography (ECC) based Multi-Factor Authentication (MFA) is employed between two entities to enhance security. The authentication is achieved using the Point multiplication operation, which provides more randomness. The three-factor authentication protocol for IoT based E-health devices is presented in this work. The architecture is coded using Verilog HDL, synthesized using Xilinx Synthesis Technology (XST) and ported in Zynq FPGA device (XC7Z020CLG484–1). The results show that the proposed three-factor mutual authentication protocol provides better security.
Continuous Authentication using Inertial-Sensors of Smartphones and Deep Learning

The legitimacy of users is of great importance for the security of information systems. The authentication process is a trade-off between system security and user experience. E.g., forced password complexity or multi-factor authentication can increase protection, but the application becomes more cumbersome for the users. Therefore, it makes sense to investigate whether the identity of a user can be verified reliably enough, without his active participation, to replace or supplement existing login processes.This master thesis examines if the inertial sensors of a smartphone can be leveraged to continuously determine whether the device is currently in possession of its legitimate owner or by another person. To this end, an approach proposed in related studies will be implemented and examined in detail. This approach is based on the use of a so-called Siamese artificial neural network to transform the measured values of the sensors into a new vector that can be classified more reliably. It is demonstrated that the reported results of the proposed approach can be reproduced under certain conditions. However, if the same model is used under conditions that are closer to a real-world application, its reliability decreases significantly. Therefore, a variant of the proposed approach is derived whose results are superior to the original model under real conditions.The thesis concludes with concrete recommendations for further development of the model and provides methodological suggestions for improving the quality of research in the topic of Continuous Authentication.
SAR image super-resolution reconstruction based on cross-resolution discrimination

High-resolution (HR) synthetic aperture radar (SAR) images play an important role in people’s daily life and military applications. However, due to the interference of speckle noise, the texture details of the SAR images become relatively blurred. The fine texture details can be reconstructed by increasing the resolution of the SAR images. Generative adversarial networks achieve high performance in image super-resolution (SR) reconstruction, but the existing generative adversarial networks only pay attention to the discrimination of HR images without that of the low-resolution (LR) images. If the reconstructed HR image is sufficiently realistic, the LR image obtained from downsampled super-resolved images should also be the same as the original LR image. To take advantage of the LR image, an SAR image SR reconstruction algorithm based on cross-resolution discrimination (CRD) using teacher–student network is proposed. First, the teacher discriminator network (TD-Net) discriminates the HR images, which enriches the reconstructed HR images with more high-frequency texture details. Second, the student discriminator network (SD-Net) discriminates the LR images, which enables the reconstructed HR images to be accurately downsampled to the original LR image. Finally, the TD-Net guides the training of the SD-Net by transmitting distillation knowledge to the SD-Net, which further improves the discriminative performance of the SD-Net. Experiments on the SAR image dataset demonstrate that the performance of the proposed CRD algorithm is better than other algorithms when both the objective evaluations and subjective effects are considered.
Applying Waveform Correlation to Reduce Seismic Analyst Workload Due to Repeating Mining Blasts

ABSTRACT Agencies that monitor for underground nuclear tests are interested in techniques that automatically characterize mining blasts to reduce the human analyst effort required to produce high-quality event bulletins. Waveform correlation is effective in finding similar waveforms from repeating seismic events, including mining blasts. We report the results of an experiment to detect and identify mining blasts for two regions, Wyoming (U.S.A.) and Scandinavia, using waveform templates recorded by multiple International Monitoring System stations of the Preparatory Commission for the Comprehensive Nuclear-Test-Ban Treaty Organization (CTBTO PrepCom) for up to 10 yr prior to the time of interest. We discuss approaches for template selection, threshold setting, and event detection that are specialized for characterizing mining blasts using a sparse, global network. We apply the approaches to one week of data for each of the two regions to evaluate the potential for establishing a set of standards for waveform correlation processing of mining blasts that can be generally applied to operational monitoring systems with a sparse network. We compare candidate events detected with our processing methods to the Reviewed Event Bulletin of the International Data Centre to assess potential reduction in analyst workload.
Facial Expression Recognition using Deep Learning

Facial expression recognition (FER) is now getting extensively popular because of its ability to predict an unknown data-set, and to its extent with some accuracy. An average human being possesses or shows seven different expressions based on the situation, namely anger, sad, happy, surprise, disgust, neutral and scared. Each individual has a unique way to express the afore-mentioned expressions and hence the term “an unknown data-set”. To identify human’s present mindset through facial expressions, many data sets are prepared based on face components (such as lips, cheek, nose, eyes and eye brows etc.,) dislocations and elasticity of all the facial parts. Many facial recognition systems are functioning on muscle distribution analysis from the mother image set’s pixel parameters. This research paper is going to present about image pre processing, facial expression learning methods, classification methods and implementation of FaceEx algorithm for facial expression analysis through FER2013 CNN data sets and Viola-Jones Principle.
Separable Complex-Valued Graph Filter Banks for Graph Signals

This paper proposes the design of orthogonal and biorthogonal separable complex-valued filter banks on graphs. The proposed filter banks adopt the structure of two channel real-valued filter banks on graphs, and the filters in it are designed as separable complex filters composed of existing real filters. In this paper, the orthogonal complex-valued filter banks satisfying aliasing cancellation, perfect reconstruction and orthogonality, and the biorthogonal complex-valued filter banks satisfying aliasing cancellation and perfect reconstruction are stated respectively. The real filters based on Bernstein polynomial with good performance are selected in the final simulation to construct the complex filter banks. Instead of designing filters directly in complex valued domain, this method can avoid the complex operation of the complex valued domain. Both the amplitude and phase of the complex wavelet coefficients of the complex filter banks can describe the edge structure of the image, which can be used in applications such as image edge detection.
Comprehensive Analysis of Variants of TF-IDF Applied on LDA and LSA Topic Modelling

Present generation is fully connected virtually through many sources of social media. In social media, opinions of people for any post, news or about any product through comments or emoticon designed to express the satisfactory note. Market standards improve on this basis. There are different online markets like Amazon, Flipkart, Myntra improve their businesses using these reviews passed. Analyzing large scale opinion or feedback of individual’s helps to identify hidden insights and work towards customer satisfaction. This paper proposes for applying different weighting scheme of TF-IDF (Term Frequency-Inverse Document Frequency) for topic modeling methods LSA and LDA to cluster the topics of discussion from large scale reviews related to booming online market ‘Amazon’. The main focus of the paper is to observe the changes in the topic modeling by applying different weighting schemes of TF-IDF. In this work topic-based models like LDA (Latent Dirichlet Allocation) and LSA (Latent Semantic Allocation) applied to various weighting schemes of TF-IDF and observed the changes of weights leads to variation of term frequency of different topics with respect to its documents. Results also show that the variation of term weights results changes in topic modeling. Visualization results of topic modeling clusters with different TF-IDF weighting schemes are presented.
Effectiveness of QR-Code in Learning during Covid-19 Pandemic

To date, learning process in universities has turned out to be much preoccupied to listen and write.Now is the time for technology innovation to be applied in learning as students on the average have a smartphone.Lecturers as tutors and books as learning resources are replaced with QR-Codes that can be accessed anytime and anywhere.The pandemic circumstance particularly supports learning innovation with QR-Code.The purpose of this research is to describe the effectiveness of using QR Code as media to optimize learning process.This research is pre-experiment with one-group pretest-posttest design.It was conducted by giving treatment to one particular class then comparing the circumstance before and after the treatment.The n-gain obtained had a value of g = 0.63 which is categorized as moderate, learning motivation has positive result, and the response of students show positive result.Hence, it implies that there is an increase in students' learning achievement after the QR code has been used in learning process.This consequently leads to a conclusion that using QR Code is effective to optimize the learning process.
High-Speed Neuromorphic Computing Using Spin-Controlled VCSELs

Reservoir computing (RC) is a neuromorphic computing paradigm that exploits the transient dynamics of a nonlinear system (the reservoir) to map input data unto a high dimensional state space. Different nonlinear systems have been proposed to serve as reservoirs, with photonic systems being particularly well-suited for RC, due to their inherent parallelism and high speed [1] . One of such photonic implementations is based on a semiconductor laser subjected to optical feedback. The high dimensional state space of this scheme is distributed temporally and can be accessed by sampling the output power along the delay line.
Comprehensive Assessment of Imbalanced Data Classification

This is an attempt to address the various challenges opportunities and scope for formulating and designing new procedure in imbalanced classification problem which poses a challenge to a predictive modelling as many of AI ML n DL algorithms which are extensively used for classification are always designed from the perspective of with majority of focus on assuming equal number of examples for a class. It leads to poor efficiency and performance especially in minority class. As Minority class is always very crucial and sensitive to classification errors and also its utmost important in imbalanced classification. This chapter discusses addresses and gives novel as well as deep insights with unequal distribution of classes in training datasets. Largely real time and real world classifications are comprising imbalanced distribution so need specialized techniques for more challenging and sophisticated models with minimal errors and improved performance.
A Novel Euler Chaotic Map for Image Encryption

A novel chaotic map based on Euler number namely Euler Map (EMAP) is suggested for image encryption algorithms (IEAs). The chaotic behavior of EMAP is appreciated with bifurcation analysis and Lyapunov Exponent (LE). The LE is the best due to the ultimate and most stable characteristic in case compared with the literature. A two-stage IEA, permutation and diffusion, is used to demonstrate the successful performance of EMAP, and this is how the success of EMAP is verified. The IEA with EMAP is examined in secure cryptanalyses and computational complexity. Some results are validated with the state of the arts. In the comparison, the suggested EMAP-based IEA stands out due to the diversity and complexity performance. It achieves a cropped image having minimum loss, a good operation time of 0.2297s and a low computational complexity. Therefore, the proposed EMAP can be applied to realistic systems.
White Blood Cell Image Classification Using Deep Learning

The microscopic inspection of blood smears provides diagnostic information concerning patients’ health status. For example, the presence of infections, leukemia, and some particular kinds of cancers can be diagnosed based on the results of the classification and the count of white blood cells. The traditional method for the differential blood count is performed by experienced operators. They use a microscope and count the percentage of the occurrence of each type of cell counted within an area of interest in smears. Obviously, this manual counting process is very tedious and slow. In addition, the cell classification and counting accuracy may depend on the capabilities and experiences of the operators. Therefore, the necessity of an automated differential counting system becomes inevitable. In this paper, CNN models are used. In order to achieve good performance from deep learning methods, the network needs to be trained with large amounts of data during the training phase. We take the images of the white blood cells for the training phase and train our model on them. With this method we achieved good accuracy than traditional methods. And we can generate the results within the seconds also.
Non-Invasive Monitoring of Gastrointestinal Tract Using Respiration Patterns

The innovation utilized in this project empowers examination of the gastrointestinal tract related framework without inconvenience or requirement for sedation, consequently forestalling the dangers of traditional endoscopy, and has the capability of urging patients to experience gastrointestinal (GI) tract observations.                In this project extra system is proposed for identification of breath out air quality and Respiratory rate is a fundamental parameter in the clinical monitoring of hospital patients. It tends to be estimated in different manners, for example, by recording chestmovements , breathing flow or pulse/heart rate. Respiratory sensor innovation permits the improvement of new sorts of advantageous and versatile respiratory rate recorders, including smart shirts, which empower increasingly effective healthcare processes in hospitals.The information is captured in the Microcontroller, and LCD shows the output, Respiratory information and breathing out quality sensor information. The screens are the real time trend of the respiratory parameter. Arrangement is additionally given for sparing the information for further analysis and investigation
Hardware Implementation of Sign Language to Text Converter Using Deep Neural Networks

The exchange of information performed in different ways such as speech, signals, or writing is called communication. Communication in deaf and mute is performed with the use of sign languages; however, they face many difficulties at the time of interacting with the common man and computers. Research is needed in this field to bring deaf-mutes more into the light of society and to increase their interaction with the common man. This paper aims at developing a system that uses Convolutional Neural Networks (CNN) and Faster R-CNN to extract the patterns in the feature vectors of each sign of twenty-six alphabets and further uses the results to recognize those signs. Different methods for the design of neural networks are proposed in this paper to enhance the performance. A standalone device is developed by implementing the pre-trained modified VGGNet models on Pi 3 A+ device.