Reviewerly/exHarmony huggingface dataset:
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer

Unsupervised text style transfer aims to alter the underlying style of the text to a desired value while keeping its style-independent semantics, without the support of parallel training corpora.Existing methods struggle to achieve both high style conversion rate and low content loss, exhibiting the over-transfer and undertransfer problems.We attribute these problems to the conflicting driving forces of the style conversion goal and content preservation goal.In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left.In our collaborative learning mechanism, each decoder is regularized by knowledge from its peer which has a different knowledge acquisition process.The difference is guaranteed by their opposite decoding directions and a distinguishability constraint.As a result, mutual knowledge distillation drives both decoders to a better optimum and alleviates the over-transfer and undertransfer problems.Experimental results on two benchmark datasets show that our framework achieves strong empirical results on both style compatibility and content preservation.
Hierarchical Contaminated Web Page Classification Based on Meta Tag Denoising Disposal

Web page classification is critical for information retrieval. Most web page classification methods have the following two faults: (1) need to analyze based on the overall web page and (2) do not pay enough attention to the existence of noise information inside the web page, which will thus decrease the efficiency and classification performance, especially when classifying the contaminated web page. To solve these problems, this paper proposes a denoising disposal algorithm. We choose the top-down method for hierarchical classification to improve the prediction efficiency. The experimental results demonstrate that our method is about 7 times faster than the full-page method and achieves good classification results in most categories. The precision of 7 parent categories is all above 88% and is 24% higher than the other meta tag-based method on average.
Cognitive functions and personality traits of students in different educational environments

Introduction. Scientists suggest that the systemic digitalization of the educational process includes the bio-psycho-social development of schoolchildren. These changes will affect the speed of reactions, the quantity and quality of perceived information, the cognitive styles used in its processing, the peculiarities of assimilating and mastering knowledge in a new digital format, compared with traditional methods of presenting information. The purpose of the study is a comparative analysis of the level of development of cognitive functions and personal characteristics of schoolchildren from educational institutions using traditional and digital learning. Materials and methods. The study involved 6th grade students from two schools in Belgorod (N = 80 schoolchildren). In one of them, learning is carried out in the traditional way, in the other, ICT is mainly used (digital interactive textbooks, interactive whiteboards and other electronic educational resources). Among the empirical methods used were observation and conversation, standardized diagnostic techniques for the study of cognitive processes, emotional and personal characteristics of adolescents. Methods of mathematical statistics (Mann-Whitney U-test,) were used to process the research results. Results and discussion. It has been empirically proven that there are differences in the level of development of cognitive functions and personal characteristics of schoolchildren from general education institutions with traditional forms of education (TS) and institutions using primarily ICT (ICTS). It has turned out that TS students have a higher level of logical thinking (р≤0,001; Uemp=398); however, the level of mental fatigue among schoolchildren from ICTS is lower (р≤0,000; Uemp=204); Teenagers from TS are less emotionally dependent on the Internet (р≤0,000; Uemp=431), and in educational activities they are more anxious (р≤0,000; Uemp=105) and emotional (р≤0,000; Uemp=225). Schoolchildren from ICTS show a great dependence on the Internet, although they admit that they sometimes have problems with both physical and mental health (р≤0,001; Uemp=453).
MiQuit SWAT CONSORT Checklist

CONSORT Checklist for the MiQuit SWAT looking at prenotification and personalisation of text messages
Towards Sentiment Analysis of Tobacco Products’ Usage in Social Media

Contemporary tobacco-related studies are mostly concerned with a single social media platform while missing out on a broader audience.Moreover, they are heavily reliant on labeled datasets, which are expensive to make.In this work, we explore sentiment and product identification on tobacco-related text from two social media platforms.We release SentiSmoke-Twitter and SentiSmoke-Reddit datasets, along with a comprehensive annotation schema for identifying tobacco products' sentiment.We then perform benchmarking text classification experiments using state-of-the-art models, including BERT, RoBERTa, and DistilBERT.Our experiments show F1 scores as high as 0.72 for sentiment identification in the Twitter dataset, 0.46 for sentiment identification, and 0.57 for product identification using semi-supervised learning for Reddit.
Less Is More: Domain Adaptation with Lottery Ticket for Reading Comprehension

In this paper, we propose a simple few-shot domain adaptation paradigm for reading comprehension.We first identify the lottery subnetwork structure within the Transformer-based source domain model via gradual magnitude pruning.Then, we only fine-tune the lottery subnetwork, a small fraction of the whole parameters, on the annotated target domain data for adaptation.To obtain more adaptable subnetworks, we introduce self-attention attribution to weigh parameters, beyond simply pruning the smallest magnitude parameters, which can be seen as combining structured pruning and unstructured magnitude pruning softly.Experimental results show that our method outperforms the full model fine-tuning adaptation on four out of five domains when only a small amount of annotated data available for adaptation.Moreover, introducing selfattention attribution reserves more parameters for important attention heads in the lottery subnetwork and improves the target domain model performance.Our further analyses reveal that, besides exploiting fewer parameters, the choice of subnetworks is critical to the effectiveness.
I can drive in Iceland: Enabling international joint analyses

In this issue of Cell Genomics, GA4GH reports key efforts to help share data across enclaves, including a framework for responsible data sharing, a data use ontology, and approaches for data use oversight. While there remains work in establishing reciprocity between data providers, we envision a future where joint analysis across enclaves is as easy as driving in different countries.
Sentiment Analysis Towards Kartu Prakerja Using Text Mining with Support Vector Machine and Radial Basis Function Kernel

Background: The introduction of Kartu Prakerja (Pre-employment Card) Programme, henceforth KPP, which was claimed to have launched in order to improve the quality of workforce, spurred controversy among members of the public. The discussion covered the amount of budget, the training materials and the operations brought out various reactions. Opinions could be largely divided into groups: the positive and the negative sentiments.Objective: This research aims to propose an automated sentiment analysis that focuses on KPP. The findings are expected to be useful in evaluating the services and facilities provided.Methods: In the sentiment analysis, Support Vector Machine (SVM) in text mining was used with Radial Basis Function (RBF) kernel. The data consisted of 500 tweets from July to October 2020, which were divided into two sets: 80% data for training and 20% data for testing with five-fold cross validation.Results: The results of descriptive analysis show that from the total 500 tweets, 60% were negative sentiments and 40% were positive sentiments. The classification in the testing data show that the average accuracy, sensitivity, specificity, negative sentiment prediction and positive sentiment prediction values were 85.20%; 91.68%; 75.75%; 85.03%; and 86.04%, respectively.Conclusion: The classification results show that SVM with RBF kernel performs well in the opinion classification. This method can be used to understand similar sentiment analysis in the future. In KPP case, the findings can inform the stakeholders to improve the programmes in the future. Keywords: Kartu Prakerja, Sentiment Analysis, Support Vector Machine, Text Mining, Radial Basis Function
Spatio-Temporal Deep Learning-Based Methods for Defect Detection: An Industrial Application Study Case

Data-driven methods—particularly machine learning techniques—are expected to play a key role in the headway of Industry 4.0. One increasingly popular application in this context is when anomaly detection is employed to test manufactured goods in assembly lines. In this work, we compare supervised, semi/weakly-supervised, and unsupervised strategies to detect anomalous sequences in video samples which may be indicative of defective televisions assembled in a factory. We compare 3D autoencoders, convolutional neural networks, and generative adversarial networks (GANs) with data collected in a laboratory. Our methodology to simulate anomalies commonly found in TV devices is discussed in this paper. We also propose an approach to generate anomalous sequences similar to those produced by a defective device as part of our GAN approach. Our results show that autoencoders perform poorly when trained with only non-anomalous data—which is important because class imbalance in industrial applications is typically skewed towards the non-anomalous class. However, we show that fine-tuning the GAN is a feasible approach to overcome this problem, achieving results comparable to those of supervised methods.
AutoChart: A Dataset for Chart-to-Text Generation Task

The analytical description of charts is an exciting and important research area with many applications in academia and industry.Yet, this challenging task has received limited attention from the computational linguistics research community.This paper proposes Au-toChart, a large dataset for the analytical description of charts, which aims to encourage more research into this important area.Specifically, we offer a novel framework that generates the charts and their analytical description automatically.We conducted extensive human and machine evaluations on the generated charts and descriptions and demonstrate that the generated texts are informative, coherent, and relevant to the corresponding charts.
Measuring Coding Challenge Competence With APPS

While programming is one of the most broadly applicable skills in modern society, modern machine learning models still cannot code solutions to basic problems. Despite its importance, there has been surprisingly little work on evaluating code generation, and it can be difficult to accurately assess code generation performance rigorously. To meet this challenge, we introduce APPS, a benchmark for code generation. Unlike prior work in more restricted settings, our benchmark measures the ability of models to take an arbitrary natural language specification and generate satisfactory Python code. Similar to how companies assess candidate software developers, we then evaluate models by checking their generated code on test cases. Our benchmark includes 10,000 problems, which range from having simple one-line solutions to being substantial algorithmic challenges. We fine-tune large language models on both GitHub and our training set, and we find that the prevalence of syntax errors is decreasing exponentially as models improve. Recent models such as GPT-Neo can pass approximately 20% of the test cases of introductory problems, so we find that machine learning models are now beginning to learn how to code. As the social significance of automatic code generation increases over the coming years, our benchmark can provide an important measure for tracking advancements.
VRSmartphoneSketch: Augmenting VR Controller With A Smartphone For Mid-air Sketching

We propose VRSmartphoneSketch, a mid-air sketching system that combines a smartphone with VR controllers to allow hybrid 2D and 3D inputs. We conducted a user study with 12 participants to explore the utility of the hybrid inputs of the bundled smartphone and controller when there was no drawing surface. The results show that the proposed system didn&#x2019;t significantly improve stroke accuracy and user experiences compared with the benchmark controller-only drawing condition. However, users have given positive feedback on the stability and sense of control of strokes brought by the smartphone&#x2019;s touch.
Color image encryption scheme based on quaternion discrete multi-fractional random transform and compressive sensing

A color image compression-encryption algorithm by combining quaternion discrete multi-fractional random transform with compressive sensing is investigated, in which the chaos-based fractional orders greatly improve key sensitivity. The original color image is compressed and encrypted with the assistance of compressive sensing, in which the partial Hadamard matrix adopted as a measurement matrix is constructed by iterating Chebyshev map instead of utilizing the entire Guassian matrix as a key. The sparse images are divided into 12 sub-images and then represented as three quaternion signals, which are modulated by the quaternion discrete multi-fractional random transform. The image blocking and the quaternion representation make the proposed cryptosystem avoid additional data extension existing in many transform-based methods. To further improve the level of security, the plaintext-related key streams generated by the 2D logistic-sine-coupling map are adopted to diffuse and confuse the intermediate results simultaneously. Consequently, the final ciphertext image is attained. Simulation results reveal that the proposed cryptosystem is feasible with high security and has strong robustness against various attacks.
An Effective Clustering Algorithm Using Adaptive Neighborhood and Border Peeling Method

Traditional clustering methods often cannot avoid the problem of selecting neighborhood parameters and the number of clusters, and the optimal selection of these parameters varies among different shapes of data, which requires prior knowledge. To address the above parameter selection problem, we propose an effective clustering algorithm based on adaptive neighborhood, which can obtain satisfactory clustering results without setting the neighborhood parameters and the number of clusters. The core idea of the algorithm is to first iterate adaptively to a logarithmic stable state and obtain neighborhood information according to the distribution characteristics of the dataset, and then mark and peel the boundary points according to this neighborhood information, and finally cluster the data clusters with the core points as the centers. We have conducted extensive comparative experiments on datasets of different sizes and different distributions and achieved satisfactory experimental results.
QFami: An Integrated Environment for Recommending Answerers on Campus

With the development of the Internet, people increasingly use search engines to obtain the answers for their questions and quench their thirst for knowledge. Although search engines often provide some information quickly, they can fail to provide what people really need. Despite the ubiquitous availability of search engines, people ask questions to other people to seek valuable local information and enrich social experiences. Existing social applications such as Stack Overflow [] and other Community Question Answering systems allow people to exchange knowledge efficiently. However, they mainly consider the expertise to recommend answerers, which would not be sufficient for supporting Q&A activities in real-world environments. In this work, we propose QFami, a novel integrated Q&A environment for physically-based Q&A scenarios on campus, QFami incorporates the interest expertise, proximity, locations, and other contextual factors that can be inferred by using various sensors on mobile phones.
Brain Strategy Algorithm for Multiple Object Tracking Based on Merging Semantic Attributes and Appearance Features

The human brain can effortlessly perform vision processes using the visual system, which helps solve multi-object tracking (MOT) problems. However, few algorithms simulate human strategies for solving MOT. Therefore, devising a method that simulates human activity in vision has become a good choice for improving MOT results, especially occlusion. Eight brain strategies have been studied from a cognitive perspective and imitated to build a novel algorithm. Two of these strategies gave our algorithm novel and outstanding results, rescuing saccades and stimulus attributes. First, rescue saccades were imitated by detecting the occlusion state in each frame, representing the critical situation that the human brain saccades toward. Then, stimulus attributes were mimicked by using semantic attributes to reidentify the person in these occlusion states. Our algorithm favourably performs on the MOT17 dataset compared to state-of-the-art trackers. In addition, we created a new dataset of 40,000 images, 190,000 annotations and 4 classes to train the detection model to detect occlusion and semantic attributes. The experimental results demonstrate that our new dataset achieves an outstanding performance on the scaled YOLOv4 detection model by achieving a 0.89 mAP 0.5.
A Real-Time Monitoring Tool for Analyzing Ethereum Digital Currency in Global Business Transaction

Ethereum (ETH) is a cryptocurrency system based on open-source blockchain technology to create a modern electronic cash transaction in decentralized network. Each node in Ethereum has an Ethereum Virtual Machine (EVM) that executes the HRPLs, similar to how Bitcoin’s structure works. Now that the Ethereum price depends on market demand, we have to identify the day-to-day ETH market price to purchase a product or perform a business transaction in Cryptocurrency Security Standard (CCSS). Digital banking services like visa and Mastercard are having difficulties accessing remote and rural areas. Our proposed work is an automated intrusion detection (AID) tool that visualizes data from the ETH blockchain in a chart structure for tracing, tracking, and clustering. We focus on the number of transactions and current ETH value in the network using the AID tool. It is easy to convert ETH value into dollars using a consensus algorithm and human-readable programming language (HRPL).
The School Library and the Promotion of Reading and Writing: Evaluation of the Reading and Writing Project “Small Readers/Future Writers”

The activities promoted by the school library play a fundamental role in promoting reading and writing. This study carried out with a group of 103 children from the 4th year of schooling of primary education of a public school in the North of Portugal, aimed to understand the impact of the school library in promoting reading by students involved in the “Small readers/Future writers”. For data collection questionnaires were applied to students. Five activities were carried out based on the work of potential children's reception The Great Word Factory, with text by Agnes De Lestrade and illustration by Valeria Docampo. The analysis of the collected data suggests that the activities promoted by the school library have an impact on the promotion of students’ reading habits, which in turn, recognize the importance of the practices developed by SL. The results prove the need to assess the impact of the pedagogical practices carried out by the school library on student learning. The results are discussed based on the literature and implications for future research/practices are recovered.
Geographical position spoofing detection based on camera sensor fingerprint

Currently, position check-in on mobile devices has become a fashionable social activity. Meanwhile, criminals probably tamper the geographical position (geo-position) information to provide an alibi. Therefore, it is of importance to identify the authenticity of geo-position. To our knowledge, many current methods for geo-position spoofing detection mainly rely on geo-position information in the database. However, these methods possibly fail in the case of missing prior information or lacking rich training samples. To address that challenge, this paper proposes an alternative manner for detecting the geo-position spoofing via camera sensor fingerprint. In particular, the camera sensor fingerprint is first extracted through the images posted by an inquiry user based on the well-designed denoising filter. Second, the authenticity of the geo-position is verified by comparing the consistency of the residual noise from newly-posted images with position check-in and the unique camera sensor fingerprint from an inquiry user. Finally, the extensive experiments are conducted on the image database, that empirically indicates the relevance of our proposed simple but effective method.
Digital literacy curriculum management in kindergarten

Education needs to formulate a curriculum that suits the digital era, especially in terms of digital literacy and cyber wellness. This research was conducted to get an overview of digital literacy curriculum management in kindergarten. A total of 122 kindergartens were purposely selected to take part in a survey of this study. Of the 122 kindergartens, 27 kindergartens have integrated digital literacy education in schools, while 95 kindergartens have no integrated digital literacy education in schools. Then, the 27 kindergartens joined a focus group discussion and the results showed that there were six valid and reliable indicators to evaluate the implementation of digital literacy curriculum management in kindergarten, they are: 1)availability of digital infrastructure; 2) competence of managers and teachers in digital literacy; 3) curriculum objectives related to mastery of basic digital literacy competencies in children; 4) digital literacy implementation in school (children’s learning experiences); 5) digital literacy teaching materials and strategies in schools and 6) parental involvement in curriculum development. In addition, there is a need for a technology integration development module in kindergarten classes that can become a guide for teachers and parents when using digital technology with children.&#x0D; &#x0D; Keywords: Curriculum, management, digital, literacy, kindergarten.
Zeroing neural network model for solving a generalized linear time-varying matrix equation

&lt;abstract&gt;&lt;p&gt;The time-varying solution of a class generalized linear matrix equation with the transpose of an unknown matrix is discussed. The computation model is constructed and asymptotic convergence proof is given by using the zeroing neural network method. Using an activation function, the predefined-time convergence property and noise suppression strategy are discussed. Numerical examples are offered to illustrate the efficacy of the suggested zeroing neural network models.&lt;/p&gt;&lt;/abstract&gt;