Reviewerly/exHarmony huggingface dataset:
A Sarong rolled around a body demonstrates that the force for separating two sheets joined by folding and rolling is very large

Abstract A lot of new science has been inspired by common phenomena and even by old traditions practiced in our daily lives. Eventually, after deep exploration, this may engender unexpected new technologies. In this paper, inspired by the wearing of a traditional cloth called a sarong , by the community in South East Asian countries and others, we investigate the behavior of sheets folded in the same way as the rolling of the sarong around the stomach. Simple equipment was designed to qualitatively collect the data which was combined with simple modeling. The rolling of the sarong around the stomach generates a joining force between two sheets, increasing proportionally to the square of the number of rolls. This finding can potentially be applied for developing a method for strongly joining sheets by simply rolling them and releasing the join by unrolling. This work can also be simply duplicated elsewhere, so it is worthy of teaching materials at undergraduate level. Both the scientific and the teaching contents can be extracted simultaneously.
Visualization of Graph-based representations for analyzing related multidimensional objects

The paper considers the task of analyzing complex interconnected objects using graphs.The subject of the research is such multidimensional object as a "politician".The paper presents the main methods of visualizing multidimensional data and the choice of data analysis is justified using graphs.An algorithm that enables to automate data collection on politicians was developed.One of the main methods of data collecting on the Internet is web scraping.Web scraping software may access the World Wide Web directly using the HTTP, or through a web browser.While web scraping can be done manually by a software user, the term typically refers to automated processes implemented using a software agent.The process of transition from an object of the "personality" type to its graph representation with various types of nodes and edges is described.The use of graphs enables to see various types of relationships, including mediate.The features of the used Gephi software for data analysis are presented.This methodology enables to change the attitude towards the analysis of multidimensional objects.
Supervised Seeded Iterated Learning for Interactive Language Learning

Language drift has been one of the major obstacles to train language models through interaction.When word-based conversational agents are trained towards completing a task, they tend to invent their language rather than leveraging natural language.In recent literature, two general methods partially counter this phenomenon: Supervised Selfplay (S2P) and Seeded Iterated Learning (SIL).While S2P jointly trains interactive and supervised losses to counter the drift, SIL changes the training dynamics to prevent language drift from occurring.In this paper, we first highlight their respective weaknesses, i.e., latestage training collapses and higher negative likelihood when evaluated on human corpus.Given these observations, we introduce Supervised Seeded Iterated Learning (SSIL) to combine both methods to minimize their respective weaknesses.We then show the effectiveness of SSIL in the language-drift translation game.
An Immersive Tractor Application for Sustainability

South Africa is currently addressing the issue of land reform. This research explores how immersive technology could serve as an intervention to avoid unintended consequences in the agricultural industry. This work used participatory design, a stage-based approach, and the SCRUM project management methodology to develop a novel tractor-based immersive technology mobile application. The tractor application developed serves to support novice farmers and learners alike, to learn about and test their skills on tractor components. Three-dimensional modelling software and EON reality software were used to develop the mobile application. Upon completion, the application was tested by experts in this domain of interest and compared to the objectives identified, resulting from land reform as set out by the Department of Agriculture. The tractor application shows promise in promoting Farming 4.0. Moreover, feedback from potential users and experts in this domain confirms the success of the application and interest in future immersive research.
WNUT 2020 Shared Task-1: Conditional Random Field(CRF) based Named Entity Recognition(NER) for Wet Lab Protocols

Detecting named entities in user generated text is a challenging task.Lab protocols specify steps in performing a lab procedure.The majority of wet lab protocols are written in noisy, dense, and domain-specific natural language.There is a growing need of automatic or semi-automatic conversion of protocols into machine-readable format to benefit biological research.The paper describes how a classifier model built using Conditional Random Field[1] detects named entities in wet lab protocols.The model 1 trained on the training data showed precision, recall and F1-score of 0.762, 0.743 and 0.752 respectively on the development set.When applied to unseen test data, the model showed 0.737, 0.640 and 0.685 respectively.
Automatic Tower Crane Lifting Path Planning Based on 4D Building Information Modeling

Lifting path planning of tower crane affect safety and efficiency of crane operations directly in a crane lifting cycle. Appropriate lifting path planning can not only avoid collision, but also save lifting time. Previous research focused on the fastest lifting path in path planning, this could be local optimum in lifting speed. But to cooperate with the fastest tower crane lifting plan, construction site may need more workers and resources, the related cost would be increased, and there would not be the global optimum in construction project level. On the other hand, 4D BIM could give effective support for construction planning and reflect the optimized schedule with resource balance in project level. Hence in real construction activities, the crane operations should follow the schedule planning in 4D BIM. Based on 4D BIM, this paper presents a method for automatic tower crane lifting path planning. We choose the concrete transport as a virtual test, the concrete is transported by hook with hopper and it has several target positions. A lifting path planning solution linked with 4D BIM is presented in this paper. Planning of securing position, transporting lifting path, releasing target position, and repositioning position in a crane lifting cycle are generated automatically in a virtual environment according to 4D BIM data. Such demonstration of our virtual test shows that the presented 4D-BIM-based automatic path planning method can generate lifting paths by following 4D BIM and give effective support to crane operations.
A Survey: Towards a Robust Deep Neural Network in Text Domain

Deep neural networks (DNNs) have achieved remarkable success in various tasks (e.g., image classification, speech recognition, and natural language processing). However, researches have shown that DNN models are vulnerable to adversarial examples, which cause incorrect predictions by adding imperceptible perturbations into normal inputs. Studies on adversarial examples in image domain have been well investigated, but in texts the research is not enough, let alone a comprehensive survey in this field. In this paper, we aim at presenting a comprehensive understanding of adversarial attacks and corresponding mitigation strategies in texts. Specifically, we first give a taxonomy of adversarial attacks and defenses in texts from the perspective of different natural language processing (NLP) tasks, and then introduce how to build a robust DNN model via testing and verification. Finally, we discuss the existing challenges of adversarial attacks and defenses in texts and present the future research directions in this emerging field.
An Exploration of Arbitrary-Order Sequence Labeling via Energy-Based Inference Networks

Many tasks in natural language processing involve predicting structured outputs, e.g., sequence labeling, semantic role labeling, parsing, and machine translation.Researchers are increasingly applying deep representation learning to these problems, but the structured component of these approaches is usually quite simplistic.In this work, we propose several high-order energy terms to capture complex dependencies among labels in sequence labeling, including several that consider the entire label sequence.We use neural parameterizations for these energy terms, drawing from convolutional, recurrent, and selfattention networks.We use the framework of learning energy-based inference networks (Tu and Gimpel, 2018) for dealing with the difficulties of training and inference with such models.We empirically demonstrate that this approach achieves substantial improvement using a variety of high-order energy terms on four sequence labeling tasks, while having the same decoding speed as simple, local classifiers.We also find high-order energies to help in noisy data conditions.
A Deep Analysis of Image Based Video Searching Techniques

For many applications like brand monitoring, it's important to search a video from large database using image as query [1].Numerous visual search technologies have emerged with the passage of time such as image to video retrieval(I2V), video to video retrieval(V2V), color base video retrieval and image to image retrieval.Video searching in large libraries has become a new area of research.Because of advance in technology, there is a need of introducing the well established searching techniques for image base video retrieval task.The main purpose of this study is to find out the best image based video retieving technquie.This research shows the importance of image base video retrieving in the searching field and addresses the problem of selecting the most accurate I2V retrieval technique.A comparison of different searching techniques is presented with respect to some characteristics to analyze and furnish a decision regarding the best among them.The accuracy and retrieval time of different techniques is different.This research shows that there are a number of visual search techniques, all those techniques perform same function in different way with different accuracy and speed.This study shows that CNN is best as compare to others techniques.In future, the best among these techniques can be implemented to reduce the searching time and produce the promising result.
Swimming with the Tide? Positional Claim Detection across Political Text Types

Manifestos are official documents of political parties, providing a comprehensive topical overview of the electoral programs.Voters, however, seldom read them and often prefer other channels, such as newspaper articles, to understand the party positions on various policy issues.The natural question to ask is how compatible these two formats (manifesto and newspaper reports) are in their representation of party positioning.We address this question with an approach that combines political science (manual annotation and analysis) and natural language processing (supervised claim identification) in a cross-text type setting: we train a classifier on annotated newspaper data and test its performance on manifestos.Our findings show a) strong performance for supervised classification even across text types and b) a substantive overlap between the two formats in terms of party positioning, with differences regarding the salience of specific issues.
User Profiles Matching for Different Social Networks Based on Faces Identification

It is common practice nowadays to use multiple social networks for different social roles. Although this, these networks assume differences in content type, communications and style of speech. If we intend to understand human behaviour as a key-feature for recommender systems, banking risk assessments or sociological researches, this is better to achieve using a combination of the data from different social media. In this paper, we propose a new approach for user profiles matching across social media based on publicly available users’ face photos and conduct an experimental study of its efficiency. Our approach is stable to changes in content and style for certain social media.
Nonequilibrium states of a plasmonic Dicke model with coherent and dissipative surface-plasmon–quantum-emitter interactions

This paper presents a theoretical examination of the non-equilibrium phase diagram of quantum emitters in a plasmonic cavity. The authors generalize driven-dissipative Dicke model to account for the coherent and dissipative interactions between the quantum emitters and the surface plasmon cavity mode. Depending on the interaction strength, the normal, superradiant, regular lasing, and lasing without inversion states are identified. Their signatures in the photon emission spectra are demonstrated.
PENERAPAN MEDIA PEMBELAJARAN MATH MOBILE LEARNING UNTUK MENINGKATKAN KEMAMPUAN PEMECAHAN MASALAH SISWA KELAS IV

This study aims are to determine the improvement of mathematical problem solving skills with the application of math mobile learning media. The study was conducted in fourth grade of SD 1 PasuruhanKidul Kudus in the academic year 2018/2019 with the number of students 19.The research method used was the classroom action research method which lasted for two cycles, namely in cycle I and cycle II. Each cycle consists of planning, implementing, observing and reflecting. The independent variable in this study is math mobile learning media, while the dependent variable is the ability to solve math problems, student learning activities, and teacher teaching skills. Data collection techniques used in this study include observation, interviews, documentation and tests. This data analysis technique uses quantitative and qualitative data analysis.The results of this study indicate that the increase is, (1) The average mathematical problem solving ability of students with the percentage of classical completeness 68.42% (enough) in the first cycle to 94.73% (good) in the second cycle, (2) Activities student learning has increased with a value of 64.48 less (need guidance) in the first cycle and 76.72 (good) in the second cycle. (3) The percentage of teaching skills of teachers has increased by a percentage of 71% (enough) in the first cycle and 80.5% (good) in the second cycle. Based on the results of research carried out in class IV SD 1 PasuruhanKidul Kudus, it can be concluded that through math mobile learning media can improve the ability to solve mathematical problems in the surrounding material and flat area, student learning activities and teaching skills of teachers in teaching and learning.
Medical Data Analysis for Different Data Types

Many discoveries and decisions in science are now being made on the basis of analyzing datasets. To gain useful information from raw medical data, data analytic uses insights to benefit the entire lifecycle of medical data. In this paper, medical data analysis notebooks are presented for collaborative and reproducible research. They provide a broad and practical introduction to medical data analysis with different data types such as images and texts. We aim to provide Jupyter notebooks to help those new to the medical data analysis field. Three exploratory coding activities including different data types are introduced: (i) Building, evaluating and interpreting deep learning models with EHR data, (ii) 2D mammogram medical imaging data analysis using CNNs for dense breasts classification, and (iii) Label recognition in radiology reports. Jupyter notebooks are useful for learning how to analyze different medical datasets and identify patterns that will improve any hospitals’ and clinicians' computer-aided medical decision-making process. Leveraging advances in exploratory data analysis in healthcare requires collaboration between clinicians and data scientists
Cybersecurity Challenges in Industry: Measuring the Challenge Solve Time to Inform Future Challenges

Cybersecurity vulnerabilities in industrial control systems have been steadily increasing over the last few years. One possible way to address this issue is through raising the awareness (through education) of software developers, with the intent to increase software quality and reduce the number of vulnerabilities. CyberSecurity Challenges (CSCs) are a novel serious game genre that aims to raise industrial software developers’ awareness of secure coding, secure coding guidelines, and secure coding best practices. An important industry-specific requirement to consider in designing these kinds of games is related to the whole event’s duration and how much time it takes to solve each challenge individually—the challenge solve time. In this work, we present two different methods to compute the challenge solve time: one method based on data collected from the CSC dashboard and another method based on a challenge heartbeat. The results obtained by both methods are presented; both methods are compared to each other, and the advantages and limitations of each method are discussed. Furthermore, we introduce the notion of a player profile, which is derived from dashboard data. Our results and contributions aim to establish a method to measure the challenge solve time, inform the design of future challenges, and improve coaching during CSC gameplay.
Structural and Tectonic Study of Qara Sard Anticline and Choplagh Syncline, from Sulaimaniya Area, Kurdistan Region, Northeastern Iraq

The study of the structural geology and tectonics of Qara Sard anticline and Goblagh syncline is carried out within the northwestern part of Zagros Fold-Thrust Belt (ZFTB) in Sulaimaniya area, Kurdistan region, Iraq.Qamchuqa Formation (Early Cretaceous) builds up the core of the anticline and overlain by Kometan, Shiranish and Tanjero formations (Late Cretaceous).Moreover, Khalakan Formation overlies Kometan Formation on the northeastern limb of the anticline.Tanjero Formation appears at the core of Choplagh syncline.The structural geometrical analyses of seven traverses (P) cutting across the anticline and the syncline, revealed that the two folds are double plunging, and asymmetrical in all traverses except in P5 and P6 in Qara Sard anticline.Both folds are verging toward northeast; however, the anticline verges toward southwest and being vertical in P4 and P5 respectively.The geometrical analysis using PI diagrams reveal that the fold axis of Qara Sard anticline is sinuous and changing its trend in three attitudes.The first is NW-SE in the traverses P1, P2 and P3 with gradual clockwise drifting.The second one is WNW-ESE in P4, P5 and P6 and then returning in the third to the first attitude NW-SE in the traverse P7.Consequently, the area was influenced by four strike-slip faults.They are either transverse or inclined to the fold axis.These faults are Dolma Reza dextral, Choplagh sinistral, Lower Zab dextral and Dokan sinistral.They divided the area into four blocks moved in opposite directions depending on the strike-slip faults displacements.On the other hand, the study folds are affected by three reverse listric faults.They are suture-verged and parallel with the fold axis.Two of them affected the northeastern limb of Qara Sard anticline, whereas the third disturbed its southwestern limb.By investigations of the vertical changes of fold style, the reactivation of the suture listric fault (2) is detected.It appears as a rotational type so as its larger effect is on traverse P7 and its displacement is gradually reduced toward P4.Moreover, the reactivation time of this suture rotational listric fault was Turonian, it is reflected throughout verging change of the fault between Qamchuqa and Kometan formations in traverse P6.
Constrained Fact Verification for FEVER

Fact-verification systems are well explored in the NLP literature with growing attention owing to shared tasks like FEVER.Though the task requires reasoning on extracted evidence to verify a claim's factuality, there is little work on understanding the reasoning process.In this work, we propose a new methodology for fact-verification, specifically FEVER, that enforces a closed-world reliance on extracted evidence.We present an extensive evaluation of state-of-the-art verification models under these constraints.
Grammaticality and Language Modelling

Ever since Pereira (2000) provided evidence against Chomsky’s (1957) conjecture that statistical language modelling is incommensurable with the aims of grammaticality prediction as a research enterprise, a new area of research has emerged that regards statistical language models as “psycholinguistic subjects” and probes their ability to acquire syntactic knowledge. The advent of The Corpus of Linguistic Acceptability (CoLA) (Warstadt et al., 2019) has earned a spot on the leaderboard for acceptability judgements, and the polemic between Lau et al. (2017) and Sprouse et al. (2018) has raised fundamental questions about the nature of grammaticality and how acceptability judgements should be elicited. All the while, we are told that neural language models continue to improve. That is not an easy claim to test at present, however, because there is almost no agreement on how to measure their improvement when it comes to grammaticality and acceptability judgements. The GLUE leaderboard bundles CoLA together with a Matthews correlation coefficient (MCC), although probably because CoLA’s seminal publication was using it to compute inter-rater reliabilities. Researchers working in this area have used other accuracy and correlation scores, often driven by a need to reconcile and compare various discrete and continuous variables with each other. The score that we will advocate for in this paper, the point biserial correlation, in fact compares a discrete variable (for us, acceptability judgements) to a continuous variable (for us, neural language model probabilities). The only previous work in this area to choose the PBC that we are aware of is Sprouse et al. (2018a), and that paper actually applied it backwards (with some justification) so that the language model probability was treated as the discrete binary variable by setting a threshold. With the PBC in mind, we will first reappraise some recent work in syntactically targeted linguistic evaluations (Hu et al., 2020), arguing that while their experimental design sets a new high watermark for this topic, their results may not prove what they have claimed. We then turn to the task-independent assessment of language models as grammaticality classifiers. Prior to the introduction of the GLUE leaderboard, the vast majority of this assessment was essentially anecdotal, and we find the use of the MCC in this regard to be problematic. We conduct several studies with PBCs to compare several popular language models. We also study the effects of several variables such as normalization and data homogeneity on PBC.
Learning Context-free Languages with Nondeterministic Stack RNNs

We present a differentiable stack data structure that simultaneously and tractably encodes an exponential number of stack configurations, based on Lang's algorithm for simulating nondeterministic pushdown automata.We call the combination of this data structure with a recurrent neural network (RNN) controller a Nondeterministic Stack RNN.We compare our model against existing stack RNNs on various formal languages, demonstrating that our model converges more reliably to algorithmic behavior on deterministic tasks, and achieves lower cross-entropy on inherently nondeterministic tasks.