Reviewerly/exHarmony huggingface dataset:
Class-agnostic Reconstruction of Dynamic Objects from Videos

We introduce REDO, a class-agnostic framework to REconstruct the Dynamic Objects from RGBD or calibrated videos. Compared to prior work, our problem setting is more realistic yet more challenging for three reasons: 1) due to occlusion or camera settings an object of interest may never be entirely visible, but we aim to reconstruct the complete shape; 2) we aim to handle different object dynamics including rigid motion, non-rigid motion, and articulation; 3) we aim to reconstruct different categories of objects with one unified framework. To address these challenges, we develop two novel modules. First, we introduce a canonical 4D implicit function which is pixel-aligned with aggregated temporal visual cues. Second, we develop a 4D transformation module which captures object dynamics to support temporal propagation and aggregation. We study the efficacy of REDO in extensive experiments on synthetic RGBD video datasets SAIL-VOS 3D and DeformingThings4D++, and on real-world video data 3DPW. We find REDO outperforms state-of-the-art dynamic reconstruction methods by a margin. In ablation studies we validate each developed component.
Towards Autonomous Robotic Precision Harvesting: Mapping, Localization, Planning and Control for a Legged Tree Harvester

This paper presents an integrated system for performing precision harvesting missions using a legged harvester. Our harvester performs a challenging task of autonomous navigation and tree grabbing in a confined, GPS denied forest environment. Strategies for mapping, localization, planning, and control are proposed and integrated into a fully autonomous system. The mission starts with a human mapping the area of interest using a custom-made sensor module. Subsequently, a human expert selects the trees for harvesting. The sensor module is then mounted on the machine and used for localization within the given map. A planning algorithm searches for both an approach pose and a path in a single path planning problem. We design a path following controller leveraging the legged harvester's capabilities for negotiating rough terrain. Upon reaching the approach pose, the machine grabs a tree with a general-purpose gripper. This process repeats for all the trees selected by the operator. Our system has been tested on a testing field with tree trunks and in a natural forest. To the best of our knowledge, this is the first time this level of autonomy has been shown on a full-size hydraulic machine operating in a realistic environment.
CodeQA: A Question Answering Dataset for Source Code Comprehension

We propose CodeQA, a free-form question answering dataset for the purpose of source code comprehension: given a code snippet and a question, a textual answer is required to be generated. CodeQA contains a Java dataset with 119,778 question-answer pairs and a Python dataset with 70,085 question-answer pairs. To obtain natural and faithful questions and answers, we implement syntactic rules and semantic analysis to transform code comments into question-answer pairs. We present the construction process and conduct systematic analysis of our dataset. Experiment results achieved by several neural baselines on our dataset are shown and discussed. While research on question-answering and machine reading comprehension develops rapidly, few prior work has drawn attention to code question answering. This new dataset can serve as a useful research benchmark for source code comprehension.
Analisis Metode Pencatatan Persediaan Pada PT. PLN (Persero) ULP. Muara Bulian Berdasarkan PSAK No. 14

The purpose of writing this Final Project is to analyze inventory records at PT. PLN (Persero) ULP Muara Bulian based on PSAK No. 14. The research method used is the Interview, Observation and Documentation method so that it can provide realistic clarity in analyzing the method of recording inventory at PT. PLN (Persero) ULP. Bulian Estuary. Based on PSAK No. 14. Data analysis uses data processing techniques and is presented descriptively. The results of the study indicate that the inventory recording carried out at PT. PLN ULP Muara Bulian is in accordance with PSAK No. 14 concerning inventories where the inventory records use a perpetual inventory system which is applied through the Application and Product in Data Processing (SAP) network system which is inputted based on the Goods Inventory Card. While the method of recording inventory at PT. PLN (Persero) ULP. Muara Bulian uses the FIFO (First In First Out) method where this method assumes that the goods that enter or are produced first are considered first to be out or sold so that the ending inventory value consists of the goods that were last entered or the last produced.
Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning

We consider the problem of online learning in the presence of distribution shifts that occur at an unknown rate and of unknown intensity. We derive a new Bayesian online inference approach to simultaneously infer these distribution shifts and adapt the model to the detected changes by integrating ideas from change point detection, switching dynamical systems, and Bayesian online learning. Using a binary 'change variable,' we construct an informative prior such that--if a change is detected--the model partially erases the information of past model updates by tempering to facilitate adaptation to the new data distribution. Furthermore, the approach uses beam search to track multiple change-point hypotheses and selects the most probable one in hindsight. Our proposed method is model-agnostic, applicable in both supervised and unsupervised learning settings, suitable for an environment of concept drifts or covariate drifts, and yields improvements over state-of-the-art Bayesian online learning approaches.
Graph Based Network with Contextualized Representations of Turns in Dialogue

Dialogue-based relation extraction (RE) aims to extract relation(s) between two arguments that appear in a dialogue. Because dialogues have the characteristics of high personal pronoun occurrences and low information density, and since most relational facts in dialogues are not supported by any single sentence, dialogue-based relation extraction requires a comprehensive understanding of dialogue. In this paper, we propose the TUrn COntext awaRE Graph Convolutional Network (TUCORE-GCN) modeled by paying attention to the way people understand dialogues. In addition, we propose a novel approach which treats the task of emotion recognition in conversations (ERC) as a dialogue-based RE. Experiments on a dialogue-based RE dataset and three ERC datasets demonstrate that our model is very effective in various dialogue-based natural language understanding tasks. In these experiments, TUCORE-GCN outperforms the state-of-the-art models on most of the benchmark datasets. Our code is available at https://github.com/BlackNoodle/TUCORE-GCN.
The effect of El Nino and La nina on the rain data of Lampung Barat Regency, Lampung Province

The purpose of this research to analyze and determine dominant impact area due to the effect of El Nino and La Nina on 6 rainfall station in west Lampung Regency, Lampung Province. This study also compred between Fast Fourier Transform (FFT) methods, and Lomb Periodogram methods. This study using rainfall data from 1998 to 2019 taken from Balai Besar Wilayah Sungai Mesuji – Sekampung (BBWSMS). According on comparison result, it is known that both methods indicate that Air Hitam Rainfall Station experience the effect of El Nino and La Nina more dominantly than the others, with the looping period of 3,9165 years from Lomb Periodogram method and 3,7381 years from (FFT) Method. It is also known that Lomb Periodogram Method has a better accuracy than (FFT) method. This is because Lomb Periodogram can read the length of data more accurately than Fast Fourier Transform (FFT) method. Keywords: El Nino, La Nina, rain station, rainfall, Fast Fourier Transform, Lomb Periodogram
Students’ perceptions of the use of the flipped classroom during the educational process of linear functions

espanolEl objetivo de esta investigacion cuantitativa es analizar el impacto del aula invertida durante el proceso de ensenanza-aprendizaje sobre las funciones lineales considerando la ciencia de datos y el aprendizaje automatico (regresion lineal). La muestra esta conformada por 72 alumnos que cursaron la asignatura Matematicas Basicas durante el ciclo escolar 2018. Los resultados del aprendizaje automatico indican que la consulta de contenidos audiovisuales antes de clase, la realizacion de los ejercicios colaborativamente durante la clase por medio de la aplicacion GeoGebra y la realizacion de las practicas de laboratorio despues de la clase por medio de la aplicacion GeoGebra influyen positivamente el desarrollo de las habilidades matematicas. La ciencia de datos identifica tres modelos predictivos sobre el uso del aula invertida y el perfil del estudiante por medio de la tecnica arbol de decision. Por ultimo, el aula invertida facilita la creacion de nuevos espacios educativos por medio del uso de la tecnologia y permite la organizacion de creativas actividades antes, durante y despues de la clase. EnglishThe purpose of this quantitative study is to analyse the impact of the flipped classroom during the teaching-learning process of linear functions considering data science and machine learning (linear regression). The sample is comprised of 72 students in the basic mathematics class during the 2018 school year. The results of the machine learning show that watching audiovisual content before class, doing exercises collaboratively during class using the application GeoGebra, and doing laboratory practices after class using the application GeoGebra have a positive influence on the development of mathematical skills. Data science identifies three predictive models on the use of the flipped classroom and the student profile using the decision tree technique. Finally, the flipped classroom facilitates the creation of new educational spaces via the use of technology and allows creative activities to be organized before, during and after class.
Analisis Metode Aliran Permukaan Langsung (Direct Runoff) Sungai Way Besai Menggunakan Software HEC-HMS

The river is a gathering place for water in the surrounding environment which flows towards a lower place. Way Besai river is located in Sumberjaya sub-district, West Lampung district which has an area of 410.72 km2. Direct runoff needs to be carried out on the Way Besai river to determine the flood hydrograph that occurs in the Way Besai river. The HEC-HMS model is a numerical hydrological model developed by the Hydrologic Engineering Center (HEC) of the US Army Corps Of Engineers. The methods used in direct flow runoff in the HEC-HMS software are the SCS UH method, the Clark's UH method and the Snyder's UH method. The purpose of this research was to analyze the Way Besai river flood hydrograph by calculating direct runoff using these three methods. The results of this study indicate that the SCS UH method is the most suitable compared to Clark's UH method and Snyder's UH method because it produces the smallest deviation value. Keywords : Way Besai, direct runoff flow, HEC-HMS, SCS UH, Clark's UH, Snyder's UH
discopy: A Neural System for Shallow Discourse Parsing

This paper demonstrates discopy, a novel framework that makes it easy to design components for end-to-end shallow discourse parsing. For the purpose of demonstration, we implement recent neural approaches and integrate contextualized word embeddings to predict explicit and non-explicit discourse relations. Our proposed neural feature-free system performs competitively to systems presented at the latest Shared Task on Shallow Discourse Parsing. Finally, a web front end is shown that simplifies the inspection of annotated documents. The source code, documentation, and pretrained models are publicly accessible.
Spectral Transform Forms Scalable Transformer

Many real-world relational systems, such as social networks and biological systems, contain dynamic interactions. When learning dynamic graph representation, it is essential to employ sequential temporal information and geometric structure. Mainstream work achieves topological embedding via message passing networks (e.g., GCN, GAT). The temporal evolution, on the other hand, is conventionally expressed via memory units (e.g., LSTM or GRU) that possess convenient information filtration in a gate mechanism. Though, such a design prevents large-scale input sequence due to the over-complicated encoding. This work learns from the philosophy of self-attention and proposes an efficient spectral-based neural unit that employs informative long-range temporal interaction. The developed spectral window unit (SWINIT) model predicts scalable dynamic graphs with assured efficiency. The architecture is assembled with a few simple effective computational blocks that constitute randomized SVD, MLP, and graph Framelet convolution. The SVD plus MLP module encodes the long-short-term feature evolution of the dynamic graph events. A fast framelet graph transform in the framelet convolution embeds the structural dynamics. Both strategies enhance the model's ability on scalable analysis. In particular, the iterative SVD approximation shrinks the computational complexity of attention to O(Nd\log(d)) for the dynamic graph with N edges and d edge features, and the multiscale transform of framelet convolution allows sufficient scalability in the network training. Our SWINIT achieves state-of-the-art performance on a variety of online continuous-time dynamic graph learning tasks, while compared to baseline methods, the number of its learnable parameters reduces by up to seven times.
LM-Critic: Language Models for Unsupervised Grammatical Error Correction

Training a model for grammatical error correction (GEC) requires a set of labeled ungrammatical / grammatical sentence pairs, but manually annotating such pairs can be expensive. Recently, the Break-It-Fix-It (BIFI) framework has demonstrated strong results on learning to repair a broken program without any labeled examples, but this relies on a perfect critic (e.g., a compiler) that returns whether an example is valid or not, which does not exist for the GEC task. In this work, we show how to leverage a pretrained language model (LM) in defining an LM-Critic, which judges a sentence to be grammatical if the LM assigns it a higher probability than its local perturbations. We apply this LM-Critic and BIFI along with a large set of unlabeled sentences to bootstrap realistic ungrammatical / grammatical pairs for training a corrector. We evaluate our approach on GEC datasets across multiple domains (CoNLL-2014, BEA-2019, GMEG-wiki and GMEG-yahoo) and show that it outperforms existing methods in both the unsupervised setting (+7.7 F0.5) and the supervised setting (+0.5 F0.5).
Evaluating the Navigation in Software Visualization in Augmented Reality with Speech Processing

Architecture of software systems is complex and abstract. Visualizing it is one approach to improve comprehension of software architecture. The recent developments inthe area of virtual reality and augmented reality allow software to be visualized in a three-dimensional environment.This representation of software architecture enables users to navigate through the different elements of an application. In Augmented Reality, gestures are used to explore thecomponents of the software and their dependencies. However, the number of gestures is limited to only a few. Therefore, a speech assistant was developed to provide users more functionalities when exploring software systems. This thesis examines the navigation in an application for software visualization in Augmented Reality using speech processing. A comparative user study, which compares the navigation with speech to gestures, is carried out. This study evaluates whether speech processing enhances the usability of software visualization in augmented reality. The objective of the present work is to discuss both the theoretical and technical aspectswhen setting up the study. Different definitions of usability are considered which serves as a foundation for the study. Furthermore, the developed speech assistant is integratedinto an application for software visualization. Based on the results of the user study, the usability of speech processing for exploring the architecture of software systems is evaluated.
SISTEM INFORMASI TOP UP SALDO PULSA BERBASIS ANDROID DENGAN JAVA DAN SQLITE DI OUTLET REZKY CELL

This writing describes the making of an Android application for the operation of buying and selling electronic pulses. This application is made to resemble an electronic notebook. The initial display of this application is in the form of the name of the provider which will display the nominal amount of credit to be filled and the transaction records that have been stored on each date and at the same time can execute credit buying and selling transactions. This application was created by the author to help people, especially sellers of electric pulses, in recording credit sales transactions and getting the necessary information easily. By filling in the data form or credit agent, credit products and customers first, users can easily record transactions that occur. From all these data, information can then be obtained in the form of a profit statement and the remainder of a customer's payment. Therefore, the purpose of the Android application for the operation of buying and selling electronic pulses is so that credit sellers using Android smartphones can quickly record their transactions and obtain the information needed for business development.
Work-in-Progress: Multiple Approximate Instances in Neural Processing Units for Energy-Efficient Circuit Synthesis

We present an architectural approach toward energy-efficient synthesis of circuits used in neural processing units. Neural network applications are shown to tolerate varying operand precisions between different inputs, accuracy targets, their phases, and learning methods, without significantly impacting the classification accuracy. Using multiple instances of systolic arrays at different precisions, we show that significant energy gains are possible beyond the conventional approach, using the same circuit for all precisions.
Formulação de definições utilizando o método Aristotélico

Abstract: Definition is an important theme in Aristotle's work. There are several types of definitions and in the context of applied ontology, the formulation of textual and formal definitions should follow the Aristotelian Principles of Genus and Differentia. The paper aims to describe the Aristotelian Principles in the formulation of definitions in ontologies and demonstrating the application of the OntoDef method. Presents a brief literature review and demonstration of the OntoDef method on terms definition of the OntONeo ontology. Application of OntoDef method bring relevant contribution to the OntONeo ontology once that helped to textually define the ontology's classes woman and pregnant woman. In addition to improving textual definitions of the ontology, as the main contribution of the OntoDef method, we highlight the clarity to identify the essence of the term to be defined, establishing the necessary and sufficient conditions for each term in question.
A teacher-student framework for online correctional learning.

A classical learning setting is one in which a student collects data, or observations, about a system, and estimates a certain quantity of interest about it. Correctional learning is a type of cooperative teacher-student framework where a teacher, who has knowledge about the system, has the possibility to observe and alter (correct) the observations received by the student in order to improve its estimation. In this paper, we show that the variance of the estimate of the student is reduced with the help of the teacher. We further formulate the online problem - where the teacher has to decide at each time instant whether or not to change the observations - as a Markov decision process, from which the optimal policy is derived using dynamic programming. We validate the framework in numerical experiments, and compare the optimal online policy with the one from the batch setting.
Research Guides: Text Mining Methods and Tools: Learn More

Learn more about text mining (aka computational text analysis) and how you can apply it to your research.
Meningkatkan Kemampuan Siswa dalam Membaca Puisi dengan Metode Demonstrasi pada Siswa Kelas VI SD Negeri 47/IV Kota Jambi Semester I Tahun Pelajaran 2020/2021

This research is motivated by the inability and lack of interest of students in reading poetry. The formulation of the research problem is whether the demonstration method can improve students' ability to read poetry in fifth grade students of SD Negeri 47/IV Jambi city in the second semester of the 2020/2021 school year? The research aims to improve students' abilities, learning outcomes, and skills in reading poetry, using the Demonstration method in accordance with the correct stages. This research was conducted in July-October 2020, at SD Negeri 47/IV Jambi City. The type of research is classroom action research with 2 cycles consisting of planning, implementation, observation and reflection. Techniques and data collection tools in this study were field notes, list of student scores and student tests. The results of this study indicate that with the Demonstration method, student learning outcomes increase by 53%. It is concluded that the Demonstration method can improve students' ability to read poetry. Keywords: Improving Ability, Reading Poetry, Demonstration
Manifold Topology Divergence: a Framework for Comparing Data Manifolds

We develop a framework for comparing data manifolds, aimed, in particular, towards the evaluation of deep generative models. We describe a novel tool, Cross-Barcode(P,Q), that, given a pair of distributions in a high-dimensional space, tracks multiscale topology spacial discrepancies between manifolds on which the distributions are concentrated. Based on the Cross-Barcode, we introduce the Manifold Topology Divergence score (MTop-Divergence) and apply it to assess the performance of deep generative models in various domains: images, 3D-shapes, time-series, and on different datasets: MNIST, Fashion MNIST, SVHN, CIFAR10, FFHQ, chest X-ray images, market stock data, ShapeNet. We demonstrate that the MTop-Divergence accurately detects various degrees of mode-dropping, intra-mode collapse, mode invention, and image disturbance. Our algorithm scales well (essentially linearly) with the increase of the dimension of the ambient high-dimensional space. It is one of the first TDA-based practical methodologies that can be applied universally to datasets of different sizes and dimensions, including the ones on which the most recent GANs in the visual domain are trained. The proposed method is domain agnostic and does not rely on pre-trained networks.
How to Explain Neural Networks: an Approximation Perspective

The lack of interpretability has hindered the large-scale adoption of AI technologies. However, the fundamental idea of interpretability, as well as how to put it into practice, remains unclear. We provide notions of interpretability based on approximation theory in this study. We first implement this approximation interpretation on a specific model (fully connected neural network) and then propose to use MLP as a universal interpreter to explain arbitrary black-box models. Extensive experiments demonstrate the effectiveness of our approach.