Reviewerly/exHarmony huggingface dataset:
Enhanced Medical Tweet Opinion Mining using Improved Dolphin Echolocation Algorithm Based Feature Selection

Extraction and analysis of public opinions from social network data can provide interesting outcomes and inferences about product, service, event or personality. Twitter is one of the most popular medium for analyzing the public sentiment through user tweets. Feature specific opinion analysis provides highly accurate and effective classification and categorization of public opinions. This paper focuses on developing an opinion mining framework for automated analysis of tweet opinions using efficient feature selection and classification algorithms. For this purpose, an Improved Dolphin Echolocation Algorithm (IDEA) is developed by enhancing the optimization performance of the Dolphin Echolocation Algorithm (DEA). The limitations of DEA are the insufficient exploration and exploitation properties in local optimum solutions and also impact the convergence rate. These shortcomings are overcome by the proposed IDEA algorithm. In this work, first the tweets are collected and pre-processed to extract the features using Part-of-Speech (POS) tagging and n-grams aided by a dictionary. Using IDEA, the feature subset candidates are selected and the outcomes are fed as input to the baseline classifiers to obtain highly accurate opinion classification. The evaluation of the k-Nearest Neighbor (KNN), Naïve Bayes (NB) and Support Vector Machine (SVM) classifiers using the two feature selection approaches of DEA and IDEA are performed over cancer and drug tweets datasets and the results illustrate that the classification accuracy of opinions is enhanced significantly through the IDEA based feature selection than the traditional DEA algorithm. These results justify the utilization of the proposed IDEA algorithm for improving the opinion mining applications in different fields.
Conceptualizing (Pre-Service) Teachers’ Professional Knowledge for Complex Domains

AbstractIn this chapter we integrate two lines of research that deal with complex knowledge domains, which are of increasing importance for pre-service teachers: Technology integration and responding to learner heterogeneity. Both domains are characterized by increased complexity, requiring pre-service teachers to understand concrete practice-related phenomena, as well as the underlying cognitive and socio-cognitive processes on a meta-level. In this context, initially we examine literature investigating the Technological Pedagogical Content Knowledge (TPCK) framework. Secondly, we provide an overview of the parameters for heterogeneity in the classroom, using three different approaches found in contemporary research. Thirdly, our considerations are integrated into both domains based on the notion of fragmented versus coherent theories. At this stage, we conceptualize how complex domains can be coherently mentally represented; arguing that teacher education needs to support pre-service teachers in their construction of professional knowledge, represented as coherent theories. Finally, we suggest an integrated perspective on pre-service teachers’ professional development based on the concept of adaptive expertise. As a practical implication, we provide a basis for teacher training to guide pre-service teachers’ meta-conceptual understanding of the potential of digital technology, and for dealing with heterogeneity in the classroom.
Deepfake videos easily fool face systems, researchers warn

A study from Switzerland's Idiap Research Institute has highlighted how vulnerable facial recognition systems are to the growing scourge of ‘deepfake’ videos – with up to 95% of deepfakes remaining undetected by advanced FR systems.
Potential Driving Factors on Surface Solar Radiation Trends over China in Recent Years

&amp;lt;p&amp;gt;The annual mean surface solar radiation (SSR) trends under all-sky, clear-sky, all-sky-no-aerosol, and clear-sky-no-aerosol conditions as well as their possible causes are analyzed during 2005-2018 over China based on different satellite-retrieved datasets to determine the likely drivers of the trends. The results confirm clouds and aerosols as the major contributors to such all-sky SSR trends over China but playing different roles over sub-regions. Aerosol variations during this period result in a widespread brightening, while cloud effects show opposite trends from south to north. Moreover, aerosols contribute more to the increasing all-sky SSR trends over northern China, while clouds dominate the SSR declines over southern China. A radiative transfer model is used to explore the relative contributions of cloud cover from different cloud types to the all-types-of-cloud-cover-induced (ACC-induced) SSR trends during this period in four typical sub-regions over China. The simulations point out that the decreases in low-cloud-cover (LCC) over the North China Plain are the largest positive contributor of all cloud types to the marked annual and seasonal ACC-induced SSR increases, and the positive contributions from both high-cloud-cover (HCC) and LCC declines in summer and winter greatly contribute to the ACC-induced SSR increases over East China. The contributions from medium-low-cloud-cover (mid-LCC) and LCC variations dominate the ACC-caused SSR trends over southwestern and South China all year round, except for the larger HCC contribution in summer.&amp;lt;/p&amp;gt;
Design of LSTM-RNN on a Sensor Based HAR using Android Phones

Activity Recognition (AR) is monitoring the liveliness of a person by using smart phone. Smart phones are used in a wider manner and it becomes one of the ways to identify the human’s environmental changes by using the sensors in smart mobiles. Smart phones are equipped in detecting sensors like compass sensor, gyroscope, GPS sensor and accelerometer. Human Activity Recognition (HAR) framework collects the raw data from sensors and observes the human movement using different classification methods. This paper focuses for Activity Recognition (AR) based on smart phone by analyzing the performance of various Deep Learning (DL) approach using in-built gyroscope and accelerometers. In this work, HAR dataset can be utilized from UCI based Machine Learning repository. The sensors such as gyroscope and accelerator are used to record the signals and performs various activities namely walking- downstairs, walking-upstairs, jogging, standing, walking and sitting while a user wearing the smartphone in a pocket. The performance metrics has analyzed to recognize user’s activities using DL approach namely Recurrent Neural Network with Long-Short Term Memory (RNN- LSTM) were applied. The result provides 96% better accuracy for RNN-LSTM with minimum Mean Absolute Percentage Error (MAPE) when compare to other machine learning classifier.
Effective Fraud Detection in Healthcare Domain using Popular Classification Modeling Techniques

Fraud is any activity with malicious intentions resulting in personal gain. In the Present Day scenario, every sector is polluted by such fraudulent activities to fetch unauthorized benefits. In HealthCare, an increase in fraudulent insurance claims has been observed over the years which may constitute around 3-5% of the total cost. Increasing healthcare costs along with the hike in fraud cases have made it difficult for people to approach these services when required. To avoid such situations, we must understand and identify such illegal acts and prepare our systems to combat such cases. Thus, there is a need to have a powerful mechanism to detect and avoid fraudulent activities. Many Data mining approaches are applied to identify, analyze and categorized fraud claims from the genuine ones. In this paper, various frauds existing in the Health Care sector have been discussed along with analyzing the effect of frauds in the health care domain with existing data mining models. Furthermost, a comparative analysis is performed on two existing approaches to extract relevant patterns related to fraudulent claims.
Image Denoising by Hybridizing Preprocessed Discrete Wavelet Transformation and Recurrent Neural Networks

In this paper, we are analyzing the performance of Recurrent Neural Network (RNN) for image recalling with improved training sets by Discrete Wavelet Transformation (DWT). DWT has been used for decomposing the images into four parts for low level feature extraction, to build the pattern information, encoding the pattern. When all the patterns of these four level training sets are encoded, and given as input to RNN to analyze the performance. This analysis is carried out in terms of successful and correct recalling of the images by hybridizing of DWT and RNN. Now we introduce salt and pepper noise so that the distorted feature vectors presented to the network. This gives a prototype pattern of noisy image and requires filtering of the training set. This leads to recalled output of the network that produces the pattern information for each part of the images. Now the integration is made possible if inverse discrete wavelet transformation (IDWT) to amalgamate the recalled outputs corresponding to each part of the image and final image is recognized.
Authentication and Key Transport Using Public Key Cryptography

It is generally regarded that there are two main potential advantages of public key techniques over symmetric cryptography. The first is that public key systems allow the straightforward definition of digital signatures, thereby enabling the service of non-repudiation which is so useful in commercial applications. The second is the simplification of key management, because there is no requirement for the online third party that is part of typical protocols based on symmetric cryptography. The first of these advantages is not really our concern in this book since non-repudiation is of limited value in authentication and key establishment. However, the second advantage has led to a great variety of new key establishment protocols since the invention of public key cryptography. In the modern distributed communications environments exemplified by the Internet, public-key-based protocols have become far more important than protocols based on symmetric cryptography.
5 Complexity reduction of electromagnetic systems

This chapter has two main objectives: first, to propose a computer-aided consistent and accurate description of the behavior of electromagnetic devices at various speeds or frequencies and, second, to describe procedures to generate compact electrical circuits for them, with an approximatively equivalent behavior.The extracted models should have a finite complexity as low as possible, while yielding an acceptable accuracy, as well as preserve essential characteristics, such as passivity.A successful complexity reduction can be obtained if a priori and on-the-fly reduction strategies are applied before and during the model discretization, followed by a posteriori complexity reduction.
Noun Identification for Tamil Language using Morphophonemic Rules

In any language, words are considered as the basic or the smallest element with a distinctive meaning. Words can be categorized into several types, depending upon their use and functions. Basically how a word changes its form to express itself in grammatical notation defines its type. The process of categorizing a word to a particular type depending upon its grammatical notation is termed as Part of Speech tagging. In this paper, an attempt has been made to identify part of speech tagging for words in Tamil language, particular to noun inflections. An algorithm has been proposed for Noun Identification for Tamil Language using Morphophonemic Rules (NIMR). A Rule based suffix stripping approach has been adopted for this implementation. The approach proposed here identifies the root word by applying various morphophonemic rules particular to suffixes. It removes the various inflections based on the set of grammatical rules available for Tamil Language and tags the word identified as a Noun. It is proposed to explore the traditional way of categorizing words in Tamil language, avoiding the influence of English grammars.
Collaborative tools web 3.0 in the teaching of mathematics in times of Covid 19 pandemic

The limited use of collaborative tools in the teaching of Mathematics with students of Middle Basic General Education has allowed us to think about the three essential axes that this work entails: collaborative tools, Didactics of Mathematics, and learning and knowledge technologies (LKT). This research aimed to analyze the use of web 3.0 collaborative tools in the teaching of Mathematics. The methodology applied with a mixed experimental-exploratory approach, through a structured questionnaire of 24 questions on a Likert scale which was validated by Cronbach&#x2019;s alpha statistic with a result of 0.846, the TAM model was applied to measure the acceptance of digital resources developed through collaborative web3.0 tools in virtual learning during the global pandemic covid-19, the ADDIE methodology was applied in the execution and application of collaborative tools for students in the eighth year of General Basic education. The contrast of the hypothesis was carried out using the Kolmogorov-Smirnov statistical test for a sample where a value less than 0.000 was obtained, stating that the development of web 3.0 resources by the teacher improves collaborative work focused on learning mathematics and, in this way, a paradigm shift is manifested in its conceptualization and modifications in its didactic development. The results show that the role of technology and the Internet in the learning of mathematics can generate some motivation, they represent for students and teachers a significant factor of high impact in the learning of mathematics in the long term, not because of their use or access to them, but for the competence to apply them in their learning.
A Blockchain-Based Framework for Transparent, Secure, and Verifiable Online Examination System

The goal of online examination is to ensure convenience, scalability, expanded reach, and customization. Despite the popularity of online exams, there have been some demonstrations against using them. The existing system is vulnerable and faces failures like question paper leakage, answer deception, trying to forge scorecards, difficulty in ensuring the integrity and consistency of scorecards by the exam board, and so on. Blockchain technology is the greatest and unsurpassed option for continuous authentication, confidentiality, and reliability. A blockchain-based framework for an online examination system is proposed to collect, register, and maintain a growing list of records using cryptography. It is also a peer-to-peer system that manages or stores time-stamped transactions such that no one can alter them. The proposed model combines time lock and multi-signature techniques and ensures the encrypted questions are locked till the exam starts, candidates’ answers are not altered, and scorecards are not manipulated. The system is open to the public, and the system’s processes are transparent. The costs associated are analyzed with the suggested solution’s on-chain and off-chain evaluation.
Fast decay of classification error in variational quantum circuits

Abstract Variational quantum circuits (VQCs) have shown great potential in near-term applications. However, the discriminative power of a VQC, in connection to its circuit architecture and depth, is not understood. To unleash the genuine discriminative power of a VQC, we propose a VQC system with the optimal classical post-processing—maximum-likelihood estimation on measuring all VQC output qubits. Via extensive numerical simulations, we find that the error of VQC quantum data classification typically decays exponentially with the circuit depth, when the VQC architecture is extensive—the number of gates does not shrink with the circuit depth. This fast error suppression ends at the saturation towards the ultimate Helstrom limit of quantum state discrimination. On the other hand, non-extensive VQCs such as quantum convolutional neural networks are sub-optimal and fail to achieve the Helstrom limit, demonstrating a trade-off between ansatz complexity and classification performance in general. To achieve the best performance for a given VQC, the optimal classical post-processing is crucial even for a binary classification problem. To simplify VQCs for near-term implementations, we find that utilizing the symmetry of the input properly can improve the performance, while oversimplification can lead to degradation.
Distributed Bandwidth Allocation Strategy for QoE Fairness of Multiple Video Streams in Bottleneck Links

In QoE fairness optimization of multiple video streams, a distributed video stream fairness scheduling strategy based on federated deep reinforcement learning is designed to address the problem of low bandwidth utilization due to unfair bandwidth allocation and the problematic convergence of distributed algorithms in cooperative control of multiple video streams. The proposed strategy predicts a reasonable bandwidth allocation weight for the current video stream according to its player state and the global characteristics provided by the server. Then the congestion control protocol allocates the proportion of available bandwidth, matching its bandwidth allocation weight to each video stream in the bottleneck link. The strategy trains a local predictive model on each client and periodically performs federated aggregation to generate the optimal global scheme. In addition, the proposed strategy constructs global parameters containing information about the overall state of the video system to improve the performance of the distributed scheduling algorithm. The experimental results show that the introduction of global parameters can improve the algorithm’s QoE fairness and overall QoE efficiency by 10% and 8%, respectively. The QoE fairness and overall QoE efficiency are improved by 8% and 7%, respectively, compared with the latest scheme.
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech

Modelling prosody variation is critical for synthesizing natural and expressive speech in endto-end text-to-speech (TTS) systems.In this paper, a cross-utterance conditional VAE (CUC-VAE) is proposed to estimate a posterior probability distribution of the latent prosody features for each phoneme by conditioning on acoustic features, speaker information, and text features obtained from both past and future sentences.At inference time, instead of the standard Gaussian distribution used by VAE, CUC-VAE allows sampling from an utterance-specific prior distribution conditioned on cross-utterance information, which allows the prosody features generated by the TTS system to be related to the context and is more similar to how humans naturally produce prosody.The performance of CUC-VAE is evaluated via a qualitative listening test for naturalness, intelligibility and quantitative measurements, including word error rates and the standard deviation of prosody attributes.Experimental results on LJ-Speech and LibriTTS data show that the proposed CUC-VAE TTS system improves naturalness and prosody diversity with clear margins.
Spatial and long–short temporal attention correlation filters for visual tracking

Discriminative correlation filter is one of the quick and effective ways for studying visual tracking. However, discriminative correlation filter-based methods still suffer from many challenging questions caused by environmental interferences, such as spatial boundary effect, temporal filter degradation, and tracking drift. A novel appearance optimisation model, named spatial and long–short temporal attention model, has been proposed based on a new spatial regularisation term and a long–short temporal regularisation term for learning the correlation filter to localise the target. On the one hand, our proposed method can improve the classical spatial regularisation term with a new weight matrix to alleviate the spatial boundary effect. On the other hand, two new temporal regularisation terms are designed: a short temporal regularisation term and a long temporal regularisation term. The short temporal regularisation term can enlarge the inner connections of the current frame and all foregoing frames to improve the tracking performances, and the long temporal regularisation term can address the influence of occlusion by using the similarity between the initial filter and the current one. Extensive experiments on various benchmarks illustrate that our proposed tracker performs favourably against several related popular trackers.
Work-In-Progress: Code Quality Issues of Computing Undergraduates

Several studies report code quality issues in academia by analysing student submissions. However, most of them focus on novices or a specific integrated development environment (IDE), and the findings might be less representative of code quality issues in general undergraduate computing. This study summarizes code quality issues from seven programming courses with various level of complexity. There are 931 assessment tasks with 15,323 Java/Python program files involved. The reported issues are specifically tailored to computing undergraduates and are selected with checkstyle (Java) and Flake8 (Python). Our study finds that students often neglect to use blank lines between code components, braces where they are optional, and a space after a comment marker. They also sometimes include too much code in one line. This initial study will be expanded via the development of a tool that can automatically summarize the code quality issues of each student submission.
Sub-Word Alignment is Still Useful: A Vest-Pocket Method for Enhancing Low-Resource Machine Translation

We leverage embedding duplication between aligned sub-words to extend the Parent-Child transfer learning method, so as to improve lowresource machine translation.We conduct experiments on benchmark datasets of My→En, Id→En and Tr→En translation scenarios.The test results show that our method produces substantial improvements, achieving the BLEU scores of 22.5, 28.0 and 18.1 respectively.In addition, the method is computationally efficient which reduces the consumption of training time by 63.8%, reaching the duration of 1.6 hours when training on a Tesla 16GB P100 GPU.All the models and source codes in the experiments will be made publicly available to support reproducible research.
Self-related primes reduce congruency effects in the Stroop task.

Theoretical accounts of self-representation assume a privileged role for information that is linked to the self and suggest that self-relevant stimuli capture attention in a seemingly obligatory manner. However, attention is not only biased toward self-relevant information, but self-relevant information might also tune attention more broadly, for instance, by engaging cognitive control processes that regulate allocation of attention. Indeed, research in social, clinical, and developmental sciences predicts a close link between a cognitive representation of the self and cognitive control processes. The present research is concerned with such a possible signaling function of the self to recruit cognitive control and tested predictions that follow from this view using the well-known Stroop task. Participants identified the print color of words. Self-reference was manipulated such that a prime was presented before or together with a Stroop word that comprised of either a possessive pronoun (e.g., "my green") or a definite/indefinite article as control (e.g., "the/a green"). Results of three experiments (Ntotal = 137) showed that self-reference priming reduced the congruency effect in the Stroop task relative to control conditions. This finding is incompatible with an attentional bias account assuming that self-relevant distractors always impair performance, but rather suggests that stimuli relevant to the self can facilitate cognitive control processes. (PsycInfo Database Record (c) 2022 APA, all rights reserved).