Reviewerly/exHarmony huggingface dataset:
Comparative Use of Videos and Images Captured by Static Telecytological Applications for Quality Control and Teleconsultation Purposes

The objective of this study was to investigate the feasibility of implementing short videos captured by static telecytological applications for remote evaluation of cervical smears prepared by means of liquid-based cytology. The study was performed on representative short videos captured from a total of 404 cervical smears that were transferred via file transfer protocol to password-protected accounts for remote review by three independent cytopathologists. Statistical evaluation of cytological diagnoses detected no significant difference in diagnostic accuracy between the diagnoses proffered on the basis of short videos and digital images. Short videos production by static telecytology applications can be used as an alternative method for telecytological diagnosis of cervical smears, particularly for quality control purposes.
Using Active Learning with Smart Board to Enhance Primary School Students' Higher Order Thinking Skills in Data Handling

Data Handling is an important topic in the Mathematics Primary Curriculum as it brings the real world of seeing data to students.However, the assessments of PISA and TIMSS indicated that the level of Malaysian students' performance and Higher Order Thinking Skills (HOTS) in Data Handling is declining.Thus, this study was designed to develop active learning instructions using smart board (ALuSB programme) and to evaluate its effectiveness in enhancing HOTS among Malaysian students in Data Handling.The study was carried out in two stages; the design and development of ALuSB programme stage based on ADDIE model and the evaluation stage to see the enhancement of HOTS in Data Handling.The research design adopted for this study was a pre-test post-test quasi-experimental design using non-equivalent control group.Quantitative and qualitative data were collected for analyses.The quantitative data was analysed using One-way ANOVA to evaluate the mean differences between the treatment groups whereas a post hoc test was further used to determine precisely which mean differences were significant and visa-versa among all the three groups.Additionally, the qualitative data was analysed using thematic analysis.The findings in this study indicated that ALuSB programme has the potential to improve students' performance and HOTS in Data Handling.Students also demonstrated increase in their ability to carry out a procedure, differentiate and organise data, make judgments and create a table or graph, making the ALuSB programme suitable to be applied in schools as it can effectively help students to enhance their HOTS.
Subverting transphobia and challenging ignorance

In this article, I present two moments of interaction emerging from a focus group between young people who are members of a community of practice: a support group for transgender youth and their parents. Using discourse analysis, I demonstrate how the young people work collaboratively to construct a mutual identity, which foregrounds their shared experience of transgender issues and minimises differences between them. I argue that they do this to actively challenge and resist the discrimination they experience due to transphobia and ignorance, which includes attempts to ‘other’ them. I show how the young people ascribe themselves agency by subverting the heteronormative ideologies which inform this othering, thus constructing an active, resistant and validated mutual identity rather than a victimised, submissive or othered one. This identity work tells us much about the hugely important role played by support groups in helping young people to construct a positive persona in the face of transphobic discrimination.
Infectious disease prevention and personal hygiene education: Focusing on the handwashing education

Objectives: This study examined the type and contents of handwashing by collecting the materials related to that of the KCDC. Through this, it proposed the strategy for implementing education projects on handwashing. Methods: This study reviewed reports of the community health survey and the handwashing project of the KCDC, and analyzed the web-based materials of the KCDC on handwashing. Forty-one health promotion data were analyzed. Results: The percentage of Korean people practicing handwashing was 81.9%-85.1% in 2019. The types of health promotion materials were stickers, card-news, leaflets, banners, posters, web documents, video, and radio soundtracks. The emphasis of the content was the proper six steps of handwashing, disease prevention, handwashing for more than 30 seconds, and handwashing with soap. The educational strategy to improve the handwashing practice was building an archive of handwashing-related materials, developing the customized content, the diversity of channels, and the activating projects through a multi-level approach. Conclusion: There is a need to enhance the importance of personal hygiene education and activate the organized activities of the community to improve handwashing practices.. Also, the scientific evidence on hand-hygiene should support the implementation of customized educational projects on various health settings.
Software Curriculum @ Siemens — The Architecture of a Training Program for Architects

Siemens established a company-wide role-based qualification and certification curriculum, focusing on the topic of “architecture”. Architects play a central role in the product lifecycle of the complex systems that Siemens offers. Since the curriculum's start in 2006 with the “senior software architect” program, we have added programs for software, system, and test architects. This curriculum is meanwhile broadly established throughout Siemens and Siemens Healthineers and other associated Siemens companies. We meanwhile have almost 700 certified architects in almost 20 countries. This industrial experience report describes the curriculum briefly but will focus on how the “architecture” of the training program for architects enabled us to evolve and extend the curriculum over time. The evolution covers not only additional roles, but also keeps the technical content up to date and adapts to modern learning formats as we strive to achieve the most impact in the available time. Our experience may be a blueprint for other key role curricula.
Design Text Mining for Anxiety Detection using Machine Learning based-on Social Media Data during COVID-19 pandemic

The COVID-19 pandemic has a profound impact on all groups, including governments, agencies, and individuals. It can make anxiety have a bad effect. So it is necessary to detect the existence of anxiety from the government to suppress and improve the community's psychology. This research aims to design text mining to detect anxiety during a pandemic by applying machine learning technology. Two methods of machine learning are designed, namely, random forest and xgboost. This design uses a sample of data from You Tube comments with a total of 4862 consisting of 3211 for negative data and 1651 for positive data. Negative data identify anxiety, while positive data identifies hope (not worry). The design of the application of this method was carried out by preliminary testing with three calculations, namely accuracy, precision, and recall. The accuracy of the Random Forest and XGB OOST methods is 83% and 73%. Meanwhile, precision and recall have an inversely proportional value. Random Forest has a precision value greater than 45% compared to xgboost. Whereas Recall, XGBOOST is bigger than ten compared to Random Forest. Random Forest can reference machine learning methods to detect someone's anxiety based on data from social media.
Errors in air-ground pilot communication: an experimental study

Understanding aviation communication has obvious implications for flight safety and for the training of pilots and Air Traffic Controllers (ATCs). Its study also sheds light on the use of Aviation English as a type of English for a very specific purpose and on real-world communication constraints in a high-stakes environment. We present results from a study combining human factors and linguistics, in which pilots were recorded in flight simulator experiments probing communication accuracy under diverse conditions. An analysis of the audio recordings in terms of whether errors were omissions or mistakes, whether errors occurred with words or numerals, and which types of aviation information were affected, revealed complex interactions between language background, pilot qualification, workload and ATC speech rate. The differences between native English speakers (NES) and non-native English speakers (NNES) are complex and more nuanced than might be expected. These results not only are relevant for aviation safety, training and testing, but they also provide insights on communication under challenging conditions and on the relation between language proficiency and cognition, with implications for second language teaching and the use of English for Specific Purposes (ESP) in highly constrained environments.
Multi-Speaker Text-to-Speech Synthesis Using Deep Gaussian Processes

Multi-speaker speech synthesis is a technique for modeling multiple speakers' voices with a single model. Although many approaches using deep neural networks (DNNs) have been proposed, DNNs are prone to overfitting when the amount of training data is limited. We propose a framework for multi-speaker speech synthesis using deep Gaussian processes (DGPs); a DGP is a deep architecture of Bayesian kernel regressions and thus robust to overfitting. In this framework, speaker information is fed to duration/acoustic models using speaker codes. We also examine the use of deep Gaussian process latent variable models (DGPLVMs). In this approach, the representation of each speaker is learned simultaneously with other model parameters, and therefore the similarity or dissimilarity of speakers is considered efficiently. We experimentally evaluated two situations to investigate the effectiveness of the proposed methods. In one situation, the amount of data from each speaker is balanced (speaker-balanced), and in the other, the data from certain speakers are limited (speaker-imbalanced). Subjective and objective evaluation results showed that both the DGP and DGPLVM synthesize multi-speaker speech more effective than a DNN in the speaker-balanced situation. We also found that the DGPLVM outperforms the DGP significantly in the speaker-imbalanced situation.
Assessing the time course of saliency and meaning: Representational similarity analysis of ERP responses to natural scenes

Low-level visual saliency is widely thought to control the allocation of overt attention within natural scenes. However, recent research has shown that the presence of meaningful information at a given location may trump saliency. Here we used representational similarity analysis (RSA) of ERP responses to natural scenes to examine the time course of the neural representation of saliency (assessed using the Graph-Based Visual Saliency model) and high-level meaning-based representations. Participants (N = 32) viewed a series of 50 different scenes, continually maintaining the most recent scene in memory. We computed the correlation between the spatial distribution of low-level salience, the correlation between the spatial distribution of higher-level meaningfulness (“meaning maps”), and the correlation between the ERP scalp distributions, for all the scenes. As we had 50 different scenes, we were able to compute a 50x50 “representational similarity matrix” (correlation matrix) for saliency, for meaning, and for each time point in the ERP waveform. We then analyzed how the relationship between the scene-related similarity matrices and the ERP-related similarity matrix evolved over time. We found that a link between the saliency-based representational space and ERP representational space emerged first (ca. 80 ms), but that a link to the meaning-based representational space emerged soon afterward (ca. 100 ms). These findings are in line with biological models of saliency and low-level visual feature processing, suggesting that meaning-related computations arise after saliency-based computations, but early enough to suppress saliency in controlling overt attention.
Unsupervised vs. Transfer Learning for Multimodal One-Shot Matching of Speech and Images

We consider the task of multimodal one-shot speech-image matching. An agent is shown a picture along with a spoken word describing the object in the picture, e.g. cookie, broccoli and After observing one paired speech-image example per class, it is shown a new set of unseen pictures, and asked to pick the ice-cream. Previous work attempted to tackle this problem using transfer learning: supervised models are trained on labelled background data not containing any of the one-shot classes. Here we compare transfer learning to unsupervised models trained on unlabelled in-domain data. On a dataset of paired isolated spoken and visual digits, we specifically compare unsupervised autoencoder-like models to supervised classifier and Siamese neural networks. In both unimodal and multimodal few-shot matching experiments, we find that transfer learning outperforms unsupervised training. We also present experiments towards combining the two methodologies, but find that transfer learning still performs best (despite idealised experiments showing the benefits of unsupervised learning).
An Optimal Noise Mechanism for Cross-correlated IoT Data Releasing

Cross correlations are ubiquitous in time-series IoT data sets such as trajectories from smartphones and smart meters data in smart grids. Conventional privacy methods have difficulty to protect cross correlation privacy within such correlated data set. Here we propose a novel Correlated noise mechanism for Cross-correlated Data Privacy (CCDP). Because the Fourier coefficients of the cross correlation of two data records are the linear product of those of the two data records, the sanitizing Fourier coefficients noise is used for efficient optimization. Also, the noise is added via the Geometric sum method, which is proved to provide the required Laplace distribution. We perform rigorous mathematical analysis of the CCDP and prove that it satisfies ε-Pufferfish privacy. We also prove that the CCDP can achieve the optimal data utility for a given privacy budget ε. What's more important, we further derive the mathematical procedure to obtain the optimal Laplace noise scale parameter to achieve better data utility. Simulations show that the proposed CCDP outperforms the independent Fourier coefficients noise mechanism, as well as two other state-of-the-art time-domain privacy mechanisms in the literature, for three types of data sets: computer-generated data, real-world trajectory data, and smart meter data.
High Performance Sequence-to-Sequence Model for Streaming Speech Recognition

Recently sequence-to-sequence models have started to achieve state-of-the-art performance on standard speech recognition tasks when processing audio data in batch mode, i.e., the complete audio data is available when starting processing. However, when it comes to performing run-on recognition on an input stream of audio data while producing recognition results in real-time and with low word-based latency, these models face several challenges. For many techniques, the whole audio sequence to be decoded needs to be available at the start of the processing, e.g., for the attention mechanism or the bidirectional LSTM (BLSTM). In this paper, we propose several techniques to mitigate these problems. We introduce an additional loss function controlling the uncertainty of the attention mechanism, a modified beam search identifying partial, stable hypotheses, ways of working with BLSTM in the encoder, and the use of chunked BLSTM. Our experiments show that with the right combination of these techniques, it is possible to perform run-on speech recognition with low word-based latency without sacrificing in word error rate performance.
Qutrit as a probe for characterization of random telegraphic noise

The behaviors of quantum Fisher information (QFI) and quantum signal to noise ratio (QSNR) of a qutrit interacting with random telegraphic noise (RTN) are studied. Analytical relations for both quantities are obtained for the ground and first excited states of qutrit as initial states and numerical procedure is followed for the superposition of these states. Every choice of the initial state leads to considerably larger values of QFI. The ground initial state can be favorable for precise estimation of the smaller values of unknown parameter of RTN for all choices of the time of interaction. Depending on the time of interaction, the excited initial state of qutrit could do better for both smaller and larger values in the slow regime of RTN. Different choices of the initial superposition state could serve better for precise estimation purposes. Our results suggest that a qutrit may perform better than a qubit as a probe for precise estimation of unknown parameters.
Sentiment Analysis of Student Review in Learning Management System Based on Sastrawi Stemmer and SVM-PSO

In the learning management system, there are reviews from students of the learning process that has been done in a period. In this case, we use the review dataset to conduct sentiment analysis. The challenge of this dataset is the number of words that contain abbreviations and are not standard. So it challenges us to test the level of accuracy in the sentiment analysis process using several classification methods and sastrawi stemmer. Sastrawi stemmer is used to reduce features without changing the meaning data, Basic function of sastrawi is change words in the basic and eliminate nonessential or non-standard words with filtering concept. In the classification process, we use the SVM-PSO algorithm and compare it with other popular classification methods such as SVM, Naive Bayes and KNN. SVM-PSO is a combination of algorithms that is good to handle data with large dimensions and binary classification types. This is our reason for using SVM-PSO as the main classifer. Experimental results show that the use of sastrawi stemmer can reduce features by 32.58%. The accuracy of the classification process using SVM-PSO of 82.27% (with sastrawi stemmer) and 82.09% (without sastrawi stemmer), these results indicate that sastrawi stemmer influences the results of classification. SVM-PSO classification method has the highest level of accuracy compared to other classification methods, namely Naive Bayes gets an accuracy of 69.73%, K-NN gets an accuracy of 77.67% and SVM gets an accuracy of 81.52%. Based on the experimental results, SVM-PSO method has the best accuracy than any other method, and Sastrawi stemmer influences the level of accuracy.
Implementation of Rumor Detection on Twitter Using the SVM Classification Method

Twitter is one of the popular social network sites, that was first launched in 2006. This service allows users to spread real-time information. However, the information obtained is not always based on facts and sometimes deliberately used to spread rumors that cause fear to the public. So detection efforts are needed to overcome and prevent the spread of rumors on Twitter. Much research regarding the detection of rumors but is limited to English and Chinese. In this study, the authors built a system to detect Indonesian-language rumors based on the implementation of the SVM classification and feature selection using the TF-IDF weighting. Data collection was conducted in November 2019 to February 2020 using crawling methods by keywords and manual labeling process. Research data used topics around government and trending with 47,449 records and features combination based on users and tweets. Stages of research include the process of collecting data on the Twitter social networking site which is then carried out preprocessing consists of case-folding, URL removal, normalization, stopwords removal, and stemming. The next stage is feature selection, N-Gram modeling, classification, and evaluation using a confusion matrix. Based on the results of the study, the system gets good performance in the test scenario using 10% of testing data and unigram features with the highest accuracy value of 78.71%. As for features twitter that affected the detection of rumors covering the number of following, the number of like and mention.&#x0D;
What2Cite: Unveiling Topics and Citations Dependencies for Scientific Literature Exploration and Recommendation

The continuous evolution of research has led to an exponential growth of the scientific literature. This engenders difficulties for researchers to entirely capture the most salient efforts related to their own research. In this paper, we propose a novel knowledge model for unveiling meaningful and labeled relations among articles based on both topics and latent citation dependencies. An experimentation on the whole literature in the Computer Science field allowed us to validate our approach by bridging the gap between few lines of textual content (e.g., an abstract) to the most relevant papers to be included in the bibliography.
Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning

We consider the task of simultaneously predicting the solar power output for the next day at half-hourly intervals using data from three related time series: solar, weather and weather forecast. We propose PSF3, a novel pattern sequence forecasting approach, an extension of the standard PSF algorithm, which uses all three time series for clustering, pattern sequence extraction and matching. We evaluate its performance on two Australian datasets from different climate zones; the results show that PSF3 is more accurate than the other PSF methods. We also investigate if a dynamic meta-learning ensemble combining the two best methods, PSF3 and a neural network, can further improve the results. We propose a new weighting strategy for combining the predictions of the ensemble members and compare it with other strategies. The overall most accurate prediction model is the meta-learning ensemble with the proposed weighting strategy.
The Principles of Organizing the Search for an Object in an Image, Tracking an Object and the Selection of Informative Features Based on the Visual Perception of a Person

A significant expansion of the scope of computer vision, in particular in real-time systems, places very high demands on them in terms of productivity and efficiency of information processing, and in feedback systems, it also requires information lag in it. Such requirements are not ensured by traditional approaches. The way out of the situation may be to use as a prototype the principles of organization of the human visual system, which has a very high selectivity of perception of video information. The paper presents a generalized dynamic model of the organization of these principles. It is proposed to use them to organize the search for an object in a coarse image of a scene, to track an object and, if necessary, to carry out its classification or recognition at a more detailed level.
Cloze Distillation Improves Psychometric Predictive Power

Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing. However, past work has also suggested dissociations between corpus probabilities and human next-word predictions. Here we evaluate several state-of-the-art language models for their match to human next-word predictions and to reading time behavior from eye movements. We then propose a novel method for distilling the linguistic information implicit in human linguistic predictions into pre-trained LMs: Cloze Distillation. We apply this method to a baseline neural LM and show potential improvement in reading time prediction and generalization to held-out human cloze data.