Reviewerly/exHarmony huggingface dataset:
Understanding content moderation systems: new methods to understand internet governance at scale, over time, and across platforms

This chapter introduces the ‘Platform Governance Observatory’, a set of tools that continuously collect public social media content and identify content that has been removed. The Observatory, developed at QUT’s Digital Media Research Centre, currently assembles random samples of public posts on YouTube, Twitter, and Instagram. Each post is collected as close as possible to the time it is posted, and its availability is tested 8–20 days later. This infrastructure allows researchers to identify which content has been removed and, depending on the information provided by each particular platform, who was responsible for removing it and why. The Observatory has been running stably since March 2018, and has so far collected usable random samples for hundreds of millions of posts to YouTube (metadata only), Twitter, and Instagram. The Platform Governance Observatory infrastructure enables scholars to undertake in-depth analysis of removal rates and to develop large-scale longitudinal analyses across multiple platforms. With this infrastructure, researchers are able to interrogate the types of content that are being moderated on social media platforms – including, importantly, measures of false negatives and false positives according to each platform’s terms of service. Uniquely, this infrastructure allows scholars to evaluate content moderation in practice and over time, as well as to measure the effects of rule changes, technical interventions, and external events on rates and removal rates of harmful content. This research infrastructure provides an important leap forward in the ongoing task of holding social media platforms to account for their content moderation practices through rigorous independent analysis, moving beyond the high-level statistics released by platforms themselves as part of regular transparency reporting.
DeepAnalyzer at SemEval-2019 Task 6: A deep learning-based ensemble method for identifying offensive tweets

This paper describes the system we developed for SemEval 2019 on Identifying and Categorizing Offensive Language in Social Media (OffensEval -Task 6).The task focuses on offensive language in tweets.It is organized into three sub-tasks for offensive language identification; automatic categorization of offense types and offense target identification.The approach for the first subtask is a deep learningbased ensemble method which uses a Bidirectional LSTM Recurrent Neural Network and a Convolutional Neural Network.Additionally we use the information from part-of-speech tagging of tweets for target identification and combine previous results for categorization of offense types.
EPIDEMIOLOGY FORECASTING ANALYSIS OF DENGUE HAEMORRAGHIC FEVER WITH SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE IN TROPICAL AREA

Background: Health problems that often occur in tropical countries are infectious diseases, one of which often causes outbreaks was Dengue Hemorrhagic Fever (DHF). This disease often causes problems especially in endemic areas and even outbreaks that occur with death from sufferers.Objectives: To forecasting of the Dengue Hemorrhagic Fever in the working area of the Puskesmas Temindung. Methods: This was analytical descriptive research with forecasting design using secondary data and primary from informant who understand the problem. Forecasting using SARIMA method (Seasonal Autoregressive Integrated Moving Average).Results: The results showed that the total of DHF cases in Temindung Health Center could be predicted by the SARIMA (1,1,1) (1,0,0) model with means square error (MSE) of 0.001394688 forecasting results obtained from October 2018 to September 2019 cases, which tend to fluctuate but illustrates an increase in cases of DHF compared to the previous year's data. Conclusion: Forecast of the DHF is for the next 12 months starting from October 2018 as many as 7 cases, in November 4 cases, in December 4 cases; then starting in January 2019 as many as 3 cases, February 2 cases, March 3 cases, April 3 cases, May 3 cases, June 4 cases, July 3 cases, August 3 cases and September 3 cases with a total number of 42. Forecasting results show dengue cases tend to fluctuate every month but have increased cases from the previous year.
JPEG steganography with particle swarm optimization accelerated by AVX

Summary Digital steganography aims at hiding secret messages in digital data transmitted over insecure channels. The JPEG format is prevalent in digital communication, and images are often used as cover objects in digital steganography. Optimization methods can improve the properties of images with embedded secret but introduce additional computational complexity to their processing. AVX instructions available in modern CPUs are, in this work, used to accelerate data parallel operations that are part of image steganography with advanced optimizations.
Public data art’s potential for digital placemaking

Data-based public art is an innovating new form of digital art which presence is increasing in the cities datascape. Data as a medium provides a special relationship with time and space by connecting the context of data mining to the one of its exhibition. The virtual component of data art opens an augmented space, where the different dimensions of data are mediated. This essay analyses how this new artform can contribute to a creative and digital placemaking of a city by offering a special sensory experience as well as renewing the storytelling of its space. Three case studies support the analysis. “Living connections”, projected on an emblematic bridge in Montreal, contributes to a spectacular placemaking. “Interconnected”, a data sculpture in Charlotte airport, relates to infrastructure placemaking. Finally, “Herald / Harbinger” connects the industrialized society with nature in a global connection. The results participate to the reflection on the nature and specificity of data art as well as enhancing its potential of transforming public space by engaging a specific relation with time, place and people.
Elicitation of software testing requirements from the selected set of software's requirements in GOREP

Requirements engineering (RE) is employed to elicit, model, and analyse the requirements of software. Software requirements elicitation is the first sub-process of RE; and it is used to identify the requirements of software according to the need of the stakeholders. In literature, we identify that goal oriented requirements elicitation process (GOREP) do not support the identification of testing requirements from the functional requirements (FR) of software in early phase of RE. Therefore, to tackle this research issue, we proposed a method for the identification of the testing requirements from the FR in GOREP. In real life applications, only those requirements are implemented and tested, which are selected by the stakeholders. So in the proposed method we used fuzzy based technique for FR selection on the basis of non-functional requirements (NFR). The 'canonical representation of multiplication operation' (CRMO) associated with L−1 R−1 inverse arithmetic principle and 'graded mean integration representation' (GMIR) based on 'triangular fuzzy numbers' (TFNs) have been applied for the selection of the FR. In our work, an Institute Examination System (IES) is used as a case study to explain the steps of the proposed method.
Digital Literacy Competence Perceptions of Classroom Teachers and Parents Regarding Themselves and Parents' Own Children

The goal of this research is to analyze digital literacy competence perceptions of classroom teachers and parents regarding themselves and parent's own children according to certain variables.Study group of this research consists of 400 classroom teachers who work in central district of Ankara province (Sincan, Etimesgut, Yenimahalle, Mamak, Keçiören), and who have accepted to fill data collection tool and 356 parents who have children in primary school.The research has been carried out in accordance with correlational survey model.As "Digital Literacy Rating Scale" has been used to determine their competence perceptions regarding the level of digital literacy of teacher and parents, SPSS 22.0 package program has been used for data analysis.The research results reveal that parents find their own levels of digital literacy as "High", but find the level of digital literacy of their children as "Medium" while the teachers find their level of digital literacy as "High".In addition to this, according to the results of the research, the seniorities of the teachers and their frequency of the Internet use have an effect on parents' perceptions on their digital literacy competence; however, this is not the case in the gender variable.The gender of parents, their educational status, and their frequency of the Internet use have affected their perceptions on their digital literacy competence.However, parents' educational status has not made a significant difference on their digital literacy competence perceptions regarding their children.Some programs that include teachers, parents and students should be organized to eliminate this difference that is seen in parents' perceptions on digital literacy competence regarding parents themselves and their children, and new researches for students should be conducted.
Effects of health promotion courses on development of healthy lifestyle behaviours and e-health literacy in nursing

Aims:The aim of this study is to determine the effects of Health Promotion courses on development of healthy lifestyle behaviors and e-Health literacy in nursing students.Methods:Between December 2015 and April 2016, this quasi-experimental study was conducted with 133 students taking a Health Promotion course in the nursing department of a university.The data were collected by pre-test and post-test questionnaires.12 weeks after baseline assessment, change in the healthy lifestyle behaviors and e-Health literacy was determined.Results:Mean participant age was 19.05±0.28 years (min=18, max=20), and 92.5% of the students were female.After the course, students achieved higher scores in the e-Health Literacy Scale (before= 27.09±4.48;after= 30.16±4.28),Health-Promoting Lifestyle Profile overall score (before= 137.75±17.09;after= 145.07±20.70)and all of its subscales; this increase was statistically significant in all subscales except Self-Actualization (before= 41.34±5.18;after= 41.37±5.99),Interpersonal Support (before= 22.01±3.30;after= 22.44±3.23)and Stress Management (before= 20.81±2.83;after= 20.92±3.48)subscales (p<0.05).A weak (r=0.294) and statistically significant (p=0.001)positive correlation was found between the final eHealth Literacy scores and Health Promoting Lifestyle Profile scores of the students. Conclusions:In conclusion, the Health Promotion course has contributed positively to development of healthy lifestyle behaviours in nursing students.The course also imparted increase of e-Health literacy.
Predicting Software Defects Using Self-Organizing Data Mining

The study predicts the software defect of ranking and classification by utilizing the self-organizing data mining method. The causal relation between software metrics and defects in software modules is established. In the analysis, software metric parameters are considered as the influencing factors and independent variables; defect label values of software modules are considered as dependent variables. When ranking is predicted during the model training process, the bugs of the defect-free modules are replaced with a negative value and those of the defective modules remain unchanged. During classification predictions, the false values of the defect-free modules are replaced with a negative value, whereas the true values of the defective modules are replaced with a positive value ≥1.5. Then, case studies and comparison based on data sets of NASA, SoftLab and Promise are conducted by imposing different algorithms. The results show that in the ranking tests, the self-organizing data mining method achieves the smallest errors. In the classification tests, the F-measure values obtained in self-organizing data mining method are the most optimal among the tested algorithms. The self-organizing data mining method is high efficiency and feasible for predicting the software defects.
The Effect of Mall Instruction on Teens' Writing Performance

This paper considers MALL as an effective instruction method to develop teens' writing performance. The results indicated participants' development of content and ideas, organization and mechanics, vocabulary, and language use. The comparison of the experimental groups (viz. Smart teens &amp; Amazing teens) with the control groups showed a noticeable development.
Designing Intelligent MIMO Nonlinear Controller Based on Fuzzy Cognitive Map Method for Energy Reduction of the Buildings

Designing a suitable controller for air-conditioning systems to reduce energy consumption and simultaneously meet the requirements of the system is very challenging. Important factors such as stability and performance of the designed controllers should be investigated to ensure the effectiveness of these controllers. In this article, the stability and performance of the fuzzy cognitive map (FCM) controller are investigated. The FCM method is used to control the direct expansion air conditioning system (DX A/C). The FCM controller has the ability to do online learning, and can achieve fast convergence thanks to its simple mathematical computation. The stability analysis of the controller was conducted using both fuzzy bidirectional associative memories (FBAMs) and the Lyapunov function. The performances of the controller were tested based on its ability for reference tracking and disturbance rejection. On the basis of the stability analysis using FBAMS and Lyapunov functions, the system is globally stable. The controller is able to track the set point faithfully, maintaining the temperature and humidity at the desired value. In order to simulate the disturbances, heat and moisture load changed to measure the ability of the controller to reject the disturbance. The results showed that the proposed controller can track the set point and has a good ability for disturbance rejection, making it an effective controller to be employed in the DX A/C system and suitable for a nonlinear robust control system.
Stylometric Classification of Ancient Greek Literary Texts by Genre

Classification of texts by genre is an important application of natural language processing to literary corpora but remains understudied for premodern and non-English traditions.We develop a stylometric feature set for ancient Greek that enables identification of texts as prose or verse.The set contains over 20 primarily syntactic features, which are calculated according to custom, language-specific heuristics.Using these features, we classify almost all surviving classical Greek literature as prose or verse with >97% accuracy and F1 score, and further classify a selection of the verse texts into the traditional genres of epic and drama.
Extraction of Message Sequence Charts from Narrative History Text

Girish Palshikar, Sachin Pawar, Sangameshwar Patil, Swapnil Hingmire, Nitin Ramrakhiyani, Harsimran Bedi, Pushpak Bhattacharyya, Vasudeva Varma. Proceedings of the First Workshop on Narrative Understanding. 2019.
Trucker Behavior Security Surveillance Based on Human Parsing

With the increase of roads and vehicles, the number of traffic accidents has gradually increased, and one of the important reasons is the driver's unsafe driving behavior.Some existing methods rely on the method of joint point estimation to identify driving behavior.This kind of method can filter out unrelated components such as background, but the joint point estimation can only give the position information of human behavior, which cannot fully describe the human body, so we try to solve this problem.For one thing, we propose a driver behavior recognition method based on human parsing which can quickly and accurately recognize the driver's unsafe behavior.For another thing, we build a trucker behavior dataset (TB dataset) with 700 videos and improve a human body segmentation model with higher precision.Finally, we achieve 93.39% and 91.32% accurate for recognizing driving with one-hand and no-hand.At the same time, we get 98.75% accuracy for recognizing driving with turned head by using ResNet50 model.
<b>BFDA</b>: A <i>MATLAB</i> Toolbox for Bayesian Functional Data Analysis

We provide a MATLAB toolbox, BFDA, that implements a Bayesian hierarchical model to smooth multiple functional data samples with the assumptions of the same underlying Gaussian process distribution, a Gaussian process prior for the mean function, and an Inverse-Wishart process prior for the covariance function.This model-based approach can borrow strength from all functional data samples to increase the smoothing accuracy, as well as simultaneously estimate the mean-covariance functions.An option of approximating the Bayesian inference process using cubic B-spline basis functions is integrated in BFDA, which allows for efficiently dealing with high-dimensional functional data.Examples of using BFDA in various scenarios and conducting follow-up functional regression are provided.The advantages of BFDA include: (1) simultaneously smooths multiple functional data samples and estimates the mean-covariance functions in a nonparametric way;(2) flexibly deals with sparse and high-dimensional functional data with stationary and nonstationary covariance functions, and without the requirement of common observation grids; (3) provides accurately smoothed functional data for follow-up analysis.
Joint Approach to Deromanization of Code-mixed Texts

The conversion of romanized texts back to the native scripts is a challenging task because of the inconsistent romanization conventions and non-standard language use. This problem is compounded by code-mixing, i.e., using words from more than one language within the same discourse. In this paper, we propose a novel approach for handling these two problems together in a single system. Our approach combines three components: language identification, back-transliteration, and sequence prediction. The results of our experiments on Bengali and Hindi datasets establish the state of the art for the task of deromanization of code-mixed texts.
A minimal Kurepa tree with respect to club embeddings

We show it is consistent with $\mathrm {GCH}$ that there is a minimal Kurepa tree with respect to club embeddings. That is, there is a Kurepa tree $T$ which club embeds in all of its Kurepa subtrees in the sense of Abraham and Shelah (1985). Moreover, the
Challenges and Expectations towards Information Literacy Skills: Voices from Teachers’ Training of Scientific Writing

This research was aiming at analyzing information literacy skills (ILS) among teachers, the challenges of mastering ILS, and the expected ILS training.The study involved 58 secondary school teachers in East Java, Indonesia.The questionnaire and the information literacy test were administered after the training to capture the data.The data were then descriptively analyzed with NVivo 12 and Minitab 16 Windows.The results have shown that more than half of all participants had scores below the standard of 70.The other findings indicated that the teachers were good at determining types of information and understanding research terminologies.However, they were still not successfully extracting what was important in mastering ILS determined by eight other indicators.To employ ILS, teachers should deal with the challenges, such as time management, motivation, and knowledge of writing.To sum up, the teachers still need more training sessions which focus on the continuity of making scientific articles for professional development.This training can even build some foundation to improve ILS to teachers.
Context Mover’s Distance & Barycenters: Optimal Transport of Contexts for Building Representations

We present a framework for building unsupervised representations of entities and their compositions, where each entity is viewed as a probability distribution rather than a vector embedding. In particular, this distribution is supported over the contexts which co-occur with the entity and are embedded in a suitable low-dimensional space. This enables us to consider representation learning from the perspective of Optimal Transport and take advantage of its tools such as Wasserstein distance and barycenters. We elaborate how the method can be applied for obtaining unsupervised representations of text and illustrate the performance (quantitatively as well as qualitatively) on tasks such as measuring sentence similarity, word entailment and similarity, where we empirically observe significant gains (e.g., 4.1% relative improvement over Sent2vec, GenSen). The key benefits of the proposed approach include: (a) capturing uncertainty and polysemy via modeling the entities as distributions, (b) utilizing the underlying geometry of the particular task (with the ground cost), (c) simultaneously providing interpretability with the notion of optimal transport between contexts and (d) easy applicability on top of existing point embedding methods. The code, as well as prebuilt histograms, are available under this https URL.
A Modal Characterization Theorem for a Probabilistic Fuzzy Description Logic

The fuzzy modality probably is interpreted over probabilistic type spaces by taking expected truth values. The arising probabilistic fuzzy description logic is invariant under probabilistic bisimilarity; more informatively, it is non-expansive wrt. a suitable notion of behavioural distance. In the present paper, we provide a characterization of the expressive power of this logic based on this observation: We prove a probabilistic analogue of the classical van Benthem theorem, which states that modal logic is precisely the bisimulation-invariant fragment of first-order logic. Specifically, we show that every formula in probabilistic fuzzy first-order logic that is non-expansive wrt. behavioural distance can be approximated by concepts of bounded rank in probabilistic fuzzy description logic.
Soccer Sonification: Enhancing Viewer Experience

We present multiple approaches to soccer soniﬁcation, focusing on enhancing the experience for a general audience. For this work, we developed our own soccer data set through computer vision analysis of footage from a tactical overhead camera. This data set included X, Y, coordinates for the ball and players throughout, as well as passes, steals and goals. After a divergent creation process, we developed four main methods of sports soniﬁcation for entertainment. For the Tempo Variation and Pitch Variation methods, tempo or pitch is operationalized to demonstrate ball and player movement data. The Key Moments method features only pass, steal and goal data, while the Musical Moments method takes ex-isting music and attempts to align the track with important data points. Evaluation was done using a combination of qualitative focus groups and quantitative surveys, with 36 participants completing hour long sessions. Results indicated an overall preference for the Pitch Variation and Musical Moments methods, and revealed a robust trade-off between usability and enjoyability.