Reviewerly/exHarmony huggingface dataset:
Table Structure Recognition using Top-Down and Bottom-Up Cues

Tables are information-rich structured objects in document images. While significant work has been done in localizing tables as graphic objects in document images, only limited attempts exist on table structure recognition. Most existing literature on structure recognition depends on extraction of meta-features from the PDF document or on the optical character recognition (OCR) models to extract low-level layout features from the image. However, these methods fail to generalize well because of the absence of meta-features or errors made by the OCR when there is a significant variance in table layouts and text organization. In our work, we focus on tables that have complex structures, dense content, and varying layouts with no dependency on meta-features and/or OCR. We present an approach for table structure recognition that combines cell detection and interaction modules to localize the cells and predict their row and column associations with other detected cells. We incorporate structural constraints as additional differential components to the loss function for cell detection. We empirically validate our method on the publicly available real-world datasets - ICDAR-2013, ICDAR-2019 (cTDaR) archival, UNLV, SciTSR, SciTSR-COMP, TableBank, and PubTabNet. Our attempt opens up a new direction for table structure recognition by combining top-down (table cells detection) and bottom-up (structure recognition) cues in visually understanding the tables.
Reading Comprehension as Natural Language Inference: A Semantic Analysis

In the recent past, Natural language Inference (NLI) has gained significant attention, particularly given its promise for downstream NLP tasks. However, its true impact is limited and has not been well studied. Therefore, in this paper, we explore the utility of NLI for one of the most prominent downstream tasks, viz. Question Answering (QA). We transform the one of the largest available MRC dataset (RACE) to an NLI form, and compare the performances of a state-of-the-art model (RoBERTa) on both these forms. We propose new characterizations of questions, and evaluate the performance of QA and NLI models on these categories. We highlight clear categories for which the model is able to perform better when the data is presented in a coherent entailment form, and a structured question-answer concatenation form, respectively.
Automated pipeline design algorithm with adaptive irrigation of agricultural facilities

In this paper are addressed necessary steps to create a costeffective pipeline system for plant irrigation. Particularly, focuses on properties of hydraulic dependencies, graph theory, plant productivity laws, which gives some information to design pipeline system. An algorithm for automated pipeline design. An important advantage of this algorithm is that possibility of changing the amount of irrigation due to the introduction of artificial intelligence in the water supply management system.
Please Mind the Root: Decoding Arborescences for Dependency Parsing

The connection between dependency trees and spanning trees is exploited by the NLP community to train and to decode graph-based dependency parsers. However, the NLP literature has missed an important difference between the two structures: only one edge may emanate from the root in a dependency tree. We analyzed the output of state-of-the-art parsers on many languages from the Universal Dependency Treebank: although these parsers are often able to learn that trees which violate the constraint should be assigned lower probabilities, their ability to do so unsurprisingly de-grades as the size of the training set decreases. In fact, the worst constraint-violation rate we observe is 24%. Prior work has proposed an inefficient algorithm to enforce the constraint, which adds a factor of n to the decoding runtime. We adapt an algorithm due to Gabow and Tarjan (1984) to dependency parsing, which satisfies the constraint without compromising the original runtime.
A New Pedagogical Focus: Exploring Extended Technique with Young Pianists

By Kasey Stokes, Piano Performance Advisor: Andy Villemez Abstract: Hungarian pianist and composer, B?la Bart?k, composed a set of pedagogical pieces called Mikrokosmos- a series of piano pieces leveled from easy to recital standard. Fast forward about forty years, American composer George Crumb composed his own set of short pieces called Makrokosmos. Although the content remains unalike, the length, title alludes to Bart?k's Mikrokosmos. Crumb was one of various composers who utilized the idea of extended technique in his compositions. Performance practice of his work Makrokosmos in particular requires the performer to mute strings by hand, pluck strings, play glissandos on the strings, place objects like chains and glass on the strings, all while singing, chanting, whistling and more. One may find this technique beneficial for young students who want to explore composition and experiment with the piano's timbre, color and sound. Students can begin experimenting by focusing on one technique at a time; this could include simply placing a piece of paper on the strings and playing or muting one note at a time. As Crumb looked to Bart?k's Mikrokosmos to help inform his compositional style, students might look to Crumb's Makrokosmos to help create pedagogical pieces that incorporate extended techniques of their own.
Morphology and ADF Based Brain Tumor Detection System from MR Images

Brain tumor is a chronic disease, which can not be correctly identified without an MRI. To minimize the contrast between consecutive pixels in order to pave the way for morphological operation on the MRI image, the image must first be filtered from this paper using the Anisotropic Diffusion Filter (ADF) to. After that, the image must be resized and converted physically to a black and white image using a threshold value. The primary filter considers potential locations for a tumor to develop. In this semi-image, morphological operations would be performed, and information on the solidity and regions of appropriate locations was obtained. The minimum value of both characters is determined on a statistical average of various tumor-containing MRI images. The key aim of this work is to classify the tumor by collecting 2D tumor image data by MRI photographs obtained from various angles of a single individual, and to examine them to show the precise 2D location of the tumor. To achieve this, for greater precision, 2D detection and segmentation of tumors has been established, so that 2D detection can be more accurate. This research work is implemented in MATLAB method with version R2015a or higher.
PENINGKATAN MINAT BELAJAR DAN PEMAHAMAN KONSEP MATEMATIKA MELALUI PEMBELAJARAN QUANTUM LEARNING

This study aims to increase interest in learning and understanding mathematical concepts of Grade VIII A students of SMP Negeri 1 Garung by applying the Quantum Learning learning model. This type of research is a classroom action research (CAR) conducted on class VIII A as many as 32 students, consisting of 10 male students and 22 female students. Data collection techniques using observation, tests, questionnaires, and field notes. The instruments used in this study were the implementation sheet of the Quantum Learning model, the observation sheet of interest in learning, the questionnaire of interest in education, and the evaluation test for concept comprehension. Analysis of the data in this study used percentages and averages. Based on the results of the research, students' interest in learning mathematics has increased; this is indicated by students not sleepy, students are no longer ashamed to ask questions, and students are excited about doing assignments and presentations. Then an increase in understanding of mathematical concepts is shown by students being able to write the exact formula of the circumference and area of a circle, students can use procedures that are appropriate to what asked in the problem, and students can answer correctly.
Perancangan Aplikasi Watermaking dalam Penyembunyian Label Hak Cipta dengan Metode RSPPMC

This watermaking is a form of steganography that is used to store a digital data into another digital host. One is used to preserve the originality or creativity of the creation of his work. While the method used in designing watermaking on digital imagery is very much, one of them is the method randomly sequenced pulseposition modulated (RSPPMC). The use of this method is done by dividing the principle image of spread spectrum and RSPPMC so that it contains 1 bit of information from the label to match the embed bits of the label. This designed software using Visual Basic programming language 6.0 has been able to do the insertion and reading of copyright labels in digital image images. The most commonly inserted character length is 250 characters, if more of the copyright labels are not perfectly stored.
Methods of teaching computer science in the system pedagogical knowledge

Abstract Modernization of education began to wear a permanent, permanent character in the modern world, since the dynamics of changes in the needs of individuals, society and the state for education is steadily growing. For quite a long time, the main subject of the modernization of school education was its content. However, in recent years, it has become increasingly clear that the center of gravity of the renewal and development of education should be shifted towards methods, innovative forms and means of education. The system-activity approach as a methodology for constructing state standards of general education closely links educational results with certain types of schoolchildren’s learning activities. It is clear that the use of new types of educational activities will force to look for new innovative forms of their implementation. The subject of the methodology of teaching informatics and a place in the vocational training system for an informatics teacherComputer science as a science and a subject in school. The connection of teaching methods of computer science with pedagogy, psychology and computer science. Methodical system of teaching computer science in a secondary school. General characteristics of its main components (goals, content of training, methods, forms and means of training).
Exploiting Unsupervised Data for Emotion Recognition in Conversations

Emotion Recognition in Conversations (ERC) aims to predict the emotional state of speakers in conversations, which is essentially a text classification task. Unlike the sentence-level text classification problem, the available supervised data for the ERC task is limited, which potentially prevents the models from playing their maximum effect. In this paper, we propose a novel approach to leverage unsupervised conversation data, which is more accessible. Specifically, we propose the Conversation Completion (ConvCom) task, which attempts to select the correct answer from candidate answers to fill a masked utterance in a conversation. Then, we Pre-train a basic COntext- Dependent Encoder (Pre-CODE) on the ConvCom task. Finally, we fine-tune the Pre-CODE on the datasets of ERC. Experimental results demonstrate that pre-training on unsupervised data achieves significant improvement of performance on the ERC datasets, particularly on the minority emotion classes.
Keynote 1: Trustworthy AI

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. Recent years have seen an astounding growth in deployment of AI systems in critical domains such as autonomous vehicles, criminal justice, healthcare, hiring, housing, human resource management, law enforcement, and public safety, where decisions taken by AI agents directly impact human lives. Consequently, there is an increasing concern if these decisions can be trusted to be correct, reliable, fair, and safe, especially under adversarial attacks. How then can we deliver on the promise of the benefits of AI but address these scenarios that have life-critical consequences for people and society? In short, how can we achieve trustworthy AI? Under the umbrella of trustworthy computing, there is a long-established framework employing formal methods and verification techniques for ensuring trust properties like reliability, security, and privacy of traditional software and hardware systems. Just as for trustworthy computing, formal verification could be an effective approach for building trust in AI-based systems. However, the set of properties needs to be extended beyond reliability, security, and privacy to include fairness, robustness, probabilistic accuracy under uncertainty, and other properties yet to be identified and defined. Further, there is a need for new property specifications and verification techniques to handle new kinds of artifacts, e.g., data distributions, probabilistic programs, and machine learning based models that may learn and adapt automatically over time. This talk will pose a new research agenda, from a formal methods perspective, for us to increase trust in AI systems.
On the diffusion of the Improved Generalized Feistel

&lt;p style='text-indent:20px;'&gt;We consider the Improved Generalized Feistel Structure (IGFS) suggested by Suzaki and Minematsu (LNCS, 2010). It is a generalization of the classical Feistel cipher. The message is divided into &lt;inline-formula&gt;&lt;tex-math id="M1"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; subblocks, a Feistel transformation is applied to each pair of successive subblocks, and then a permutation of the subblocks follows. This permutation affects the diffusion property of the cipher. IGFS with relatively big &lt;inline-formula&gt;&lt;tex-math id="M2"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and good diffusion are of particular interest for light weight applications.&lt;/p&gt;&lt;p style='text-indent:20px;'&gt;Suzaki and Minematsu (LNCS, 2010) study the case when one and the same permutation is applied at each round, while we consider IGFS with possibly different permutations at the different rounds. In this case we present permutation sequences yielding IGFS with the best known by now diffusion for all even &lt;inline-formula&gt;&lt;tex-math id="M3"&gt;\begin{document}$ k\le 2048 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. For &lt;inline-formula&gt;&lt;tex-math id="M4"&gt;\begin{document}$ k\le 16 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; they are found by a computer-aided search, while for &lt;inline-formula&gt;&lt;tex-math id="M5"&gt;\begin{document}$ 18\le k\le 2048 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; we first consider several recursive constructions of a permutation sequence for &lt;inline-formula&gt;&lt;tex-math id="M6"&gt;\begin{document}$ k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; subblocks from two permutation sequences for &lt;inline-formula&gt;&lt;tex-math id="M7"&gt;\begin{document}$ k_a&amp;lt; k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; and &lt;inline-formula&gt;&lt;tex-math id="M8"&gt;\begin{document}$ k_b&amp;lt; k $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt; subblocks respectively. Using computer, we apply these constructions to obtain permutation sequences with good diffusion for each even &lt;inline-formula&gt;&lt;tex-math id="M9"&gt;\begin{document}$ k\le 2048 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;. Finally we obtain infinite families of permutation sequences for &lt;inline-formula&gt;&lt;tex-math id="M10"&gt;\begin{document}$ k&amp;gt;2048 $\end{document}&lt;/tex-math&gt;&lt;/inline-formula&gt;.&lt;/p&gt;
Secure Data Sharing Using Two Fold Cryptography Key Protection, Proxy Re-encryption and Key Separation Techniques

In Data transmissions Expertise, Data sharing in cloud storage is getting considerable responsiveness, as it could offer consumers using well-organized as well as operational storing facilities. The cryptographic methods are generally practiced for protecting the privacy of the collective complex information. On the other hand for information distribution, the information safety remains quiet posturing important encounters in cloud storage. How to defend as well as withdraw the cryptographic basic remains the essential trial amongst them. We recommend a novel information safety device for cloud storage for tackling this that embraces the resulting properties. 1) Using twofold aspects, the cryptographic basic remains safeguarded. The privacy of the cryptographic basic remains detained, on condition that one of the dual features works. 2) In incorporating the substitution re-encoding as well as basic departure methods, the cryptographic basic could remain withdrawn proficiently. 3) Through implementing the characteristic grounded encoding method, the information is endangered in a finegrained approach. Moreover, the safety inquiry as well as presentation assessment demonstrate that our suggestion remains safe as well as well-organized, correspondingly.
Desenvolvimento de um Protótipo Automatizado de Detecção e Focalização de Raios Solares com Hardware Livre

Solar energy is a renewable and inexhaustible source, besidescausing damage to nature, being clean and sustainable.Transform the electromagnetic radiation emitted by the Sunelectrical energy are used solar panels. In order to improveefficiency and performance of this capture, a low-cost wasbuilt, a single-axis solar tracking system for photovoltaicpanels. The solution uses the automation Arduino UNO R3,open hardware, two photosensitive sensors LDR GL-5528, inaddition to a servo motor capable of moving the surface of aphotovoltaic plate according to the detection of the highestincidence of light. The circuit and its components wereprogrammed using the Arduino IDE software, version 1.8.11.As a result, it was possible to follow the movement of thesun, differing from a static panel, thus ensuring greater sunshineon the solar plate, as a result of this traceablecontrol prototype.
Galileo at SemEval-2020 Task 12: Multi-lingual Learning for Offensive Language Identification using Pre-trained Language Models

This paper describes Galileo's performance in SemEval-2020 Task 12 on detecting and categorizing offensive language in social media. For Offensive Language Identification, we proposed a multi-lingual method using Pre-trained Language Models, ERNIE and XLM-R. For offensive language categorization, we proposed a knowledge distillation method trained on soft labels generated by several supervised models. Our team participated in all three sub-tasks. In Sub-task A - Offensive Language Identification, we ranked first in terms of average F1 scores in all languages. We are also the only team which ranked among the top three across all languages. We also took the first place in Sub-task B - Automatic Categorization of Offense Types and Sub-task C - Offence Target Identification.
Research on Tibetan Handwritten Numerals Recognition Based on TextCaps Model with Few Training Sample

In recent years, machine learning has been widely used in image recognition while conventional models such as linear classifiers, K-nearest neighbors, non-linear classifiers, and Support Vector Machines (SVM) all require a large number of training samples to train the model to achieve good results.Previous researches on handwritten numerals recognition in Tibetan also need a large number of training samples to train the model.Therefore, this paper conducts training and recognition research on TextCaps model proposed by Vinoj Jayasundara et al., by using Tibetan handwritten numerals data set with a few training samples.In this paper, the training samples of 200 tags for each class are used to add random noise to the original image instantiation parameters and generate new image data sets through the Capsule Network (CapsNet) and its decoder network to achieve the purpose of data expansion.Finally, the character images are classified by the a new model.
Improving the Efficiency of Grammatical Error Correction with Erroneous Span Detection and Correction

We propose a novel language-independent approach to improve the efficiency for Grammatical Error Correction (GEC) by dividing the task into two subtasks: Erroneous Span Detection (ESD) and Erroneous Span Correction (ESC). ESD identifies grammatically incorrect text spans with an efficient sequence tagging model. Then, ESC leverages a seq2seq model to take the sentence with annotated erroneous spans as input and only outputs the corrected text for these spans. Experiments show our approach performs comparably to conventional seq2seq approaches in both English and Chinese GEC benchmarks with less than 50% time cost for inference.
Significance of Human Capital on the Economic Growth of the Gambia

Since time immemorial, economic growth has been a chief concern for macroeconomic policymakers in every country. The significance of human capital in achieving these concerns is making waves in the space of contemporary development economics. Much theoretical and empirical evidence has shown that human capital accumulation (education and health) and their collective capabilities are a vital variable for economic development. This paper investigates the existing literature on the importance of human capital on the economic growth of the Gambia from 1990- 2019. Using annual time series economic data, we applied the econometric techniques of the ADF test to prove stationarity, rejection, and acceptance of the hypothesis of Granger causality and rejection of the null hypothesis of Johansen’s cointegration test. The Vector Error Correction Model (VECM) model was conducted, and the findings indicated a positive relationship between education and GDP in the long-run and a bidirectional relationship in the short-run. Thus, we recommended the policymakers to create strategic and prudent policies that would promote accessible and quality education with modern technologies to improve the economic growth of The Gambia. We concluded on the premise that investment in people translates to a country's economic growth.
Unconstrained Text Detection in Manga

The detection and recognition of unconstrained text is an open problem in research. Text in comic books has unusual styles that raise many challenges for text detection. This work aims to identify text characters at a pixel level in a comic genre with highly sophisticated text styles: Japanese manga. To overcome the lack of a manga dataset with individual character level annotations, we create our own. Most of the literature in text detection use bounding box metrics, which are unsuitable for pixel-level evaluation. Thus, we implemented special metrics to evaluate performance. Using these resources, we designed and evaluated a deep network model, outperforming current methods for text detection in manga in most metrics.
Aprendendo Conceitos de Orientação a Objetos Usando as Ferramentas Scratch e Snap!

The object-oriented programming paradigm serves as the basisfor the most used programming languages nowadays, where largescale projects take advantage of certain characteristics, such ascode reuse, encapsulation, better organization on the system design,and consequently the improvement on the maintainabilityof software in general. These features are intrinsic in the objectorientedparadigm. However, preparing professionals to work assystems analysts or developers for projects based in this paradigmhas been shown difficult in academia, since it is necessary to understanda set of abstract concepts before starting the codification phase.In this context, this paper seeks to contribute with the study of twoprogramming tools – Scratch and Snap! – developed exclusivelyfor teaching computer programming, exploring them in order toteach object-oriented concepts in a visual and interactive mode,aiming to apply these concepts later in a traditional programminglanguage such as Java, C#, or C++.