Reviewerly/exHarmony huggingface dataset:
Public Blockchains Scalability: An Examination of Sharding and Segregated Witness

Recently, public and permissionless blockchains such as Bitcoin and Ethereum have been facing a formidable challenge in the form of scalability which has hindered their expected growth. Both Bitcoin and Ethereum can process fewer than 20 transactions per second, which is significantly lower than their centralized counterpart such as VISA which can process approximately 1700 transactions per second. In realizing this hindrance for wide range adoption of blockchains for building advanced and large scalable systems, the blockchain community has proposed several solutions including Sharding and Segregated Witness (SegWit). Although these proposals are innovative, they still suffer from the blockchain trilemma of scalability, security, and decentralization. Moreover, at this time, little is known or discussed regarding factors related to design choices, feasibility, limitations, and other issues in adopting these solutions in public and permissionless blockchains. Hence, this paper provides the first comprehensive state-of-the-art review of sharding and segregated witness in public and permissionless blockchains, identifying current advancements, highlighting their limitations and discussing possible remedies for the overall improvement of the blockchain domain.
The effectiveness of Quizlet application towards students’ motivation in learning vocabulary

Increasing motivation is necessary for students in learning English vocabulary. The higher motivation the students gain, the bigger success they achieve in mastering vocabulary. However, most students in senior high schools have less motivation in learning vocabulary, especially in memorizing them. Thus, the teachers need to discover appropriate media for increasing their motivation. Some applications can be used through the smartphone as the need for vocabulary learning. The purpose of this study is to investigate the effectiveness of using Quizlet Application as the media for increasing the students’ motivation in learning vocabulary. Nonequivalent control group design was used in this research. The participants were 65 students who were divided into the experimental and the control groups. The students’ motivation was observed during the process of vocabulary learning by using the observation checklist. In processing the data, the Wilcoxon test was used. The results show the use of Quizlet Application is effective to increase the students’ motivation in learning vocabulary. Students felt more enthusiastic, did not get bored easily, and thus showed high interest when learning vocabulary via the app. They also did the tasks provided in that application independently.
Hessian with Mini-Batches for Electrical Demand Prediction

The steepest descent method is frequently used for neural network tuning. Mini-batches are commonly used to get better tuning of the steepest descent in the neural network. Nevertheless, steepest descent with mini-batches could be delayed in reaching a minimum. The Hessian could be quicker than the steepest descent in reaching a minimum, and it is easier to achieve this goal by using the Hessian with mini-batches. In this article, the Hessian is combined with mini-batches for neural network tuning. The discussed algorithm is applied for electrical demand prediction.
Placement of Unmanned Aerial Vehicles for Directional Coverage in 3D Space

This paper considers the fundamental problem of Placement of unmanned Aerial vehicles achieviNg 3D Directional coverAge (PANDA), that is, given a set of objects with determined positions and orientations in a 3D space, deploy a fixed number of UAVs by adjusting their positions and orientations such that the overall directional coverage utility for all objects is maximized. First, we establish the 3D directional coverage model for both cameras and objects. Then, we propose a Dominating Coverage Set (DCS) extraction method to reduce the infinite solution space of PANDA to a limited one without performance loss. Finally, we model the reformulated problem as maximizing a monotone submodular function subject to a matroid constraint and present a greedy algorithm with 1- 1/e approximation ratio to address this problem. We conduct simulations and field experiments to evaluate the proposed algorithm, and the results show that our algorithm outperforms comparison ones by at least 75.4%.
Preliminary Study on the Knowledge Graph Construction of Chinese Ancient History and Culture

The domestic population has paid increasing attention to ancient Chinese history and culture with the continuous improvement of people’s living standards, the rapid economic growth, and the rapid advancement of information science and technology. The use of information technology has been proven to promote the spread and development of historical culture, and it is becoming a necessary means to promote our traditional culture. This paper will build a knowledge graph of ancient Chinese history and culture in order to facilitate the public to more quickly and accurately understand the relevant knowledge of ancient Chinese history and culture. The construction process is as follows: firstly, use crawler technology to obtain text and table data related to ancient history and culture on Baidu Encyclopedia (similar to Wikipedia) and ancient Chinese history and culture related pages. Among them, the crawler technology crawls the semi-structured data in the information box (InfoBox) in the Baidu Encyclopedia to directly construct the triples required for the knowledge graph, crawls the introductory text information of the entries in Baidu Encyclopedia, and specialized historical and cultural websites (history Chunqiu.com, On History.com) to extract unstructured entities and relationships. Secondly, entity recognition and relationship extraction are performed on an unstructured text. The entity recognition part uses the Bidirectional Long Short-Term Memory-Convolutional Neural Networks-Conditions Random Field (BiLSTM-CNN-CRF) model for entity extraction. The relationship extraction between entities is performed by using the open source tool DeepKE (information extraction tool with language recognition ability developed by Zhejiang University) to extract the relationships between entities. After obtaining the entity and the relationship between the entities, supplement it with the triple data that were constructed from the semi-structured data in the existing knowledge base and Baidu Encyclopedia information box. Subsequently, the ontology construction and the quality evaluation of the entire constructed knowledge graph are performed to form the final knowledge graph of ancient Chinese history and culture.
LINSPECTOR: Multilingual Probing Tasks for Word Representations

Despite an ever-growing number of word representation models introduced for a large number of languages, there is a lack of a standardized technique to provide insights into what is captured by these models. Such insights would help the community to get an estimate of the downstream task performance, as well as to design more informed neural architectures, while avoiding extensive experimentation that requires substantial computational resources not all researchers have access to. A recent development in NLP is to use simple classification tasks, also called probing tasks, that test for a single linguistic feature such as part-of-speech. Existing studies mostly focus on exploring the linguistic information encoded by the continuous representations of English text. However, from a typological perspective the morphologically poor English is rather an outlier: The information encoded by the word order and function words in English is often stored on a subword, morphological level in other languages. To address this, we introduce 15 type-level probing tasks such as case marking, possession, word length, morphological tag count, and pseudoword identification for 24 languages. We present a reusable methodology for creation and evaluation of such tests in a multilingual setting, which is challenging because of a lack of resources, lower quality of tools, and differences among languages. We then present experiments on several diverse multilingual word embedding models, in which we relate the probing task performance for a diverse set of languages to a range of five classic NLP tasks: POS-tagging, dependency parsing, semantic role labeling, named entity recognition, and natural language inference. We find that a number of probing tests have significantly high positive correlation to the downstream tasks, especially for morphologically rich languages. We show that our tests can be used to explore word embeddings or black-box neural models for linguistic cues in a multilingual setting. We release the probing data sets and the evaluation suite LINSPECTOR with https://github.com/UKPLab/linspector .
FTT-NAS: Discovering Fault-Tolerant Neural Architecture

With the fast evolvement of deep-learning specific embedded computing systems, applications powered by deep learning are moving from the cloud to the edge. When deploying NNs onto the edge devices under complex environments, there are various types of possible faults: soft errors caused by atmospheric neutrons and radioactive impurities, voltage instability, aging, temperature variations, and malicious attackers. Thus the safety risk of deploying neural networks at edge computing devices in safety-critic applications is now drawing much attention. In this paper, we implement the random bit-flip, Gaussian, and Salt-and-Pepper fault models and establish a multi-objective fault-tolerant neural architecture search framework. On top of the NAS framework, we propose Fault-Tolerant Neural Architecture Search (FT-NAS) to automatically discover convolutional neural network (CNN) architectures that are reliable to various faults in nowadays edge devices. Then we incorporate fault-tolerant training (FTT) in the search process to achieve better results, which we called FTT-NAS. Experiments show that the discovered architecture FT-NAS-Net and FTT-NAS-Net outperform other hand-designed baseline architectures (58.1%/86.6% VS. 10.0%/52.2%), with comparable FLOPs and less parameters. What is more, the architectures trained under a single fault model can also defend against other faults. By inspecting the discovered architecture, we find that there are redundant connections learned to protect the sensitive paths. This insight can guide future fault-tolerant neural architecture design, and we verify it by a modification on ResNet-20-ResNet-M.
Invisible crack visualization and depth analysis by mechanoluminescence film

We experimentally demonstrated the depth analysis of artificial invisible notches on the backside of aluminum alloy (A6061-T6) plate using a high-sensitive mechanoluminescence (ML) film. The ML film was developed by a screen-printing method utilizing a mixture of Eu-doped SrAl2O4 powder and optical epoxy resin. From the analysis of luminescence distribution images (ML patterns) obtained from the ML film attached to the aluminum alloy specimen, the ML film visualized the notches of 37.5–100% depth levels from the opposite side of the notched surface of the specimen under dynamic tensile testing. Moreover, it was clarified that the discovered two parameters from the ML patterns are worthwhile to estimate the depth level of the invisible notch. The first parameter is the ratio of the time derivative of the ML intensity curves located on the stress concentration area and nominal stress area. The other is the distance between two points with maximum ML intensity located around the back-side of the invisible notch. These parameters were found to vary almost linearly with the depth level of the back-side notch, although their detection ranges are different. Also, it should be noted that the second parameter is hardly affected by the strain magnitude and its rate. This implies that the high-sensitive ML film is a promising candidate as non-destructive testing to predict a residual life of a fatigue crack in a structure under dynamic behavior.
Gendered hate speech in YouTube and YouNow comments: Results of two content analyses

Online hate speech in general, and gendered online hate speech in particular, have become an issue of growing concern both in public and academic discourses. However, although YouTube is the most important social media platform today and the popularity of social live streaming services (SLSS) such as Twitch, Periscope and YouNow is constantly growing, research on gendered online hate speech on video platforms is scarce. To bridge this empirical gap, two studies investigated gendered online hate speech in video comments on YouTube and YouNow, thereby systematically replicating a study by Wotanis and McMillan (2014). Study 1 investigated YouTube in the form of a content analysis of N = 8,000 publicly available video comments that were addressed towards four pairs of female and male German-speaking YouTubers within the popular genres Comedy, Gaming, HowTo &amp; Style, and Sports [Fitness]. Study 2 examined YouNow, with a quantitative content analysis of N = 6,844 publicly available video comments made during the video streams of 16 female and 14 male popular German-speaking YouNowers. Study 1 successfully replicated the findings of Wotanis and McMillan (2014) that compared to male You-Tubers, female YouTubers received more negative video comments (including sexist, racist, and sexually aggressive hate speech) (H1a). In addition, they received fewer positive video comments regarding personality and video content but more positive video comments regarding physical appearance (H2a). Study 2 partly confirmed the earlier findings: It found that, compared to male YouNowers, the video comments received by female YouNowers were more sexist and sexually aggressive, but not generally more hostile or negative (H1b). They received more positive video comments regarding their physical appearance but did not receive fewer positive video comments regarding their personality or the content of their videos (H2b). With some exceptions, the findings of study 2 were comparable to the findings of study 1 (RQ1). In both studies, most effect sizes were small. Overall, females on the video platforms YouTube and YouNow seem to be disproportionately affected by both hostile and benevolent sexism expressed in viewer comments. The results are in line with the Expectation States Theory and the Ambivalent Sexism Theory. The total number of public hate comments was probably underestimated because inappropriate comments can be deleted by moderators and users. Future research directions and practical implications are discussed. Supplementary material can be retrieved from https://osf.io/da8tw
MitosisNet: End-to-End Mitotic Cell Detection by Multi-Task Learning

Mitotic cell detection is one of the challenging problems in the field of computational pathology.Currently, mitotic cell detection and counting are one of the strongest prognostic markers for breast cancer diagnosis.The clinical visual inspection on histology slides is tedious, error prone, and time consuming for the pathologist.Thus, automatic mitotic cell detection approaches are highly demanded in clinical practice.In this paper, we propose an end-to-end multi-task learning system for mitosis detection from pathological images which is named ''MitosisNet''.MitosisNet consist of segmentation, detection, and classification models where the segmentation, and detection models are used for mitosis reference region detection and the classification model is applied for further confirmation of the mitosis regions.In addition, an integrated multi-patch reference scheme and a novel confidence analysis strategy are introduced for improving overall detection performance during testing.The proposed system is evaluated on three different publicly available datasets including MITOSIS 2012, MITOSIS 2014, and Case Western Reserve University (CWRU) datasets.The experimental results demonstrate state-of-the-art performance compared to the existing methods and the proposed approach is fast enough in order to meet the requirements of clinical practice.
Identification of authorship of Ukrainian-language texts of journalistic style using neural networks

The problem of development of an effective method for text authorship identification (on the material of publications of well-known Ukrainian journalists) is explored. Most existing methods require text preprocessing, which entails new costs when solving the set problem. In the case where the number of possible authors can be minimized, this approach is often excessive. Another disadvantage of the existing approaches is that their vast majority was applied to texts in foreign languages and did not take into consideration the peculiarities of the Ukrainian language. Therefore, it was decided to develop an approach that makes it possible to identify the author of the text in Ukrainian without preprocessing and give high accuracy results, as well as to establish what types of artificial neural networks provide the minimum error for Ukrainian publicists.The developed method uses a multilayer perceptron of direct distribution, the algorithm of supervised learning, vectorization HashingVectorizer, and Adam optimizer. It was determined that with a small number of iterations (4–5 iterations) of artificial neural network learning, we obtain a rather high accuracy of identification of authorship of journalistic texts and rather small value of error. Over 1,000 fragments of texts by three Ukrainian authors were used. As a result of the conducted experiments, it was found that the application of the developed approach to solving the set problem enables achieving rather high results. In the texts containing not less than 500 characters, the accuracy reaches 91 %, and the maximum number of iterations of artificial neural network learning does not exceed 15. Such results were achieved primarily due to the efficient selection of the vectorization method at the preparatory stage and the structure of an artificial neural network
The Remoteness of Remote Learning

The measures enforced by governments to contain the highly contagious COVID19 pathogen laid bare the deep inequalities that beset education systems around the world. The lockdowns and subsequent closures of educational institutions have amplified the gap between the rich and the poor, not just between the Global North and the Global South, but within countries as well. As of April 6th, UNESCO reported that 188 countries have temporarily closed its educational institutions, while several countries implemented localized closures, affecting 1,576,021,818 learners. Accordingly, education authorities have urged for classes at all levels to be moved online, a sudden but necessary emergency response to COVID19. However, for disadvantaged groups, the problem is how to meet the basic conditions that remote learning requires. &#x0D; &#x0D; &#x0D;
Deep Graph-Based Multimodal Feature Embedding for Endomicroscopy Image Retrieval

Representation learning is a critical task for medical image analysis in computer-aided diagnosis. However, it is challenging to learn discriminative features due to the limited size of the data set and the lack of labels. In this article, we propose a deep graph-based multimodal feature embedding (DGMFE) framework for medical image retrieval with application to breast tissue classification by learning discriminative features of probe-based confocal laser endomicroscopy (pCLE). We first build a multimodality graph model based on the visual similarity between pCLE data and reference histology images. The latent similar pCLE-histology pairs are extracted by walking with the cyclic path on the graph while the dissimilar pairs are extracted based on the geodesic distance. Given the similar and dissimilar pairs, the latent feature space is discovered by reconstructing the similarity between pCLE and histology images via deep Siamese neural networks. The proposed method is evaluated on a clinical database with 700 pCLE mosaics. The accuracy of image retrieval demonstrates that DGMFE can outperform previous works on feature learning. Especially, the top-1 accuracy in an eight-class retrieval task is 0.739, thus demonstrating a 10% improvement compared to the state-of-the-art method.
Probabilistic outlier detection for sparse multivariate geotechnical site investigation data using Bayesian learning

Various uncertainties arising during acquisition process of geoscience data may result in anomalous data instances (i.e., outliers) that do not conform with the expected pattern of regular data instances. With sparse multivariate data obtained from geotechnical site investigation, it is impossible to identify outliers with certainty due to the distortion of statistics of geotechnical parameters caused by outliers and their associated statistical uncertainty resulted from data sparsity. This paper develops a probabilistic outlier detection method for sparse multivariate data obtained from geotechnical site investigation. The proposed approach quantifies the outlying probability of each data instance based on Mahalanobis distance and determines outliers as those data instances with outlying probabilities greater than 0.5. It tackles the distortion issue of statistics estimated from the dataset with outliers by a re-sampling technique and accounts, rationally, for the statistical uncertainty by Bayesian machine learning. Moreover, the proposed approach also suggests an exclusive method to determine outlying components of each outlier. The proposed approach is illustrated and verified using simulated and real-life dataset. It showed that the proposed approach properly identifies outliers among sparse multivariate data and their corresponding outlying components in a probabilistic manner. It can significantly reduce the masking effect (i.e., missing some actual outliers due to the distortion of statistics by the outliers and statistical uncertainty). It also found that outliers among sparse multivariate data instances affect significantly the construction of multivariate distribution of geotechnical parameters for uncertainty quantification. This emphasizes the necessity of data cleaning process (e.g., outlier detection) for uncertainty quantification based on geoscience data.
A Hierarchical Model for Data-to-Text Generation

Transcribing structured data into natural language descriptions has emerged as a challenging task, referred to as "data-to-text". These structures generally regroup multiple elements, as well as their attributes. Most attempts rely on translation encoder-decoder methods which linearize elements into a sequence. This however loses most of the structure contained in the data. In this work, we propose to overpass this limitation with a hierarchical model that encodes the data-structure at the element-level and the structure level. Evaluations on RotoWire show the effectiveness of our model w.r.t. qualitative and quantitative metrics.
Perceptions of ImmerseMe Virtual Reality Platform to Improve English Communicative Skills in Higher Education

The main objective of this project was to evaluate the impact of the application of the virtual reality platform ImmerseMe as an empowering and innovative tool for learning English in a private university, as well as evaluating its possible future implementation in a private university in Medellín. It was applied in speaking activities to measure and evaluate the level of speaking of students from levels 1 to 3, as a pilot test for the use of immersive virtual reality within the thematic units currently designed from the department of foreign languages and cultures. The findings of the study showed that an immersive VR platform like this one is ideal to enhance the different skills of English as a foreign language (EFL) from an immersive focus considering different contexts and thinking of the development of communicative skills and interaction with native speakers in higher education. The recommendations given are for teachers and students’ participation and motivation for its implementation contemplating the cost and the multiple advantages of immersing students in a second language virtual environment.