Reviewerly/exHarmony huggingface dataset:
Application of Asset Management in the Management of Irrigation Areas in the Salaman Technical Implementation Unit

One of the main factors for supporting development in the agricultural sector is the irrigation infrastructure management. Infrastructure is essential because it is a basic prerequisite for development and growth and requires very large costs. Therefore, the infrastructure must be managed properly according to Asset Management principles. This study aims to implement asset management in the management of Irrigation Area in the Salaman Technical Implementation Unit (UPT). The irrigation assets in question are the physical infrastructure of irrigation networks. This study requires an inventory of data on irrigation network which is used as input in analyzing the performance appraisal of irrigation assets. The assessment of the Irrigation Area Performance index is carried out through the weighting of each component according to the technical instructions set by the government. While the evaluation is carried out by determining priorities and types of handling irrigation areas through ranking and classification of aset condition index value. The evaluation show that 3 (three) Irrigation Areas get a condition index value of ≤ 60 (poor). The three irrigation areas are MA Depok, DI Pakem I, and MA Mudal. MA Depok is an irrigation area that is the first priority for treatment in the form of rehabilitation.
Contrastive Reinforcement Learning of Symbolic Reasoning Domains

Abstract symbolic reasoning, as required in domains such as mathematics and logic, is a key component of human intelligence. Solvers for these domains have important applications, especially to computer-assisted education. But learning to solve symbolic problems is challenging for machine learning algorithms. Existing models either learn from human solutions or use hand-engineered features, making them expensive to apply in new domains. In this paper, we instead consider symbolic domains as simple environments where states and actions are given as unstructured text, and binary rewards indicate whether a problem is solved. This flexible setup makes it easy to specify new domains, but search and planning become challenging. We introduce four environments inspired by the Mathematics Common Core Curriculum, and observe that existing Reinforcement Learning baselines perform poorly. We then present a novel learning algorithm, Contrastive Policy Learning (ConPoLe) that explicitly optimizes the InfoNCE loss, which lower bounds the mutual information between the current state and next states that continue on a path to the solution. ConPoLe successfully solves all four domains. Moreover, problem representations learned by ConPoLe enable accurate prediction of the categories of problems in a real mathematics curriculum. Our results suggest new directions for reinforcement learning in symbolic domains, as well as applications to mathematics education.
Attacks against Ranking Algorithms with Text Embeddings: A Case Study on Recruitment Algorithms

Recently, some studies have shown that text classification tasks are vulnerable to poisoning and evasion attacks. However, little work has investigated attacks against decision-making algorithms that use text embeddings, and their output is a ranking. In this paper, we focus on ranking algorithms for the recruitment process that employ text embeddings for ranking applicants’ resumes when compared to a job description. We demonstrate both white-box and black-box attacks that identify text items that, based on their location in embedding space, have a significant contribution in increasing the similarity score between a resume and a job description. The adversary then uses these text items to improve the ranking of their resume among others. We tested recruitment algorithms that use the similarity scores obtained from Universal Sentence Encoder (USE) and Term Frequency–Inverse Document Frequency (TF-IDF) vectors. Our results show that in both adversarial settings, on average the attacker is successful. We also found that attacks against TF-IDF are more successful compared to USE.
Sentiment Analysis with a Text Blob Package Implications for Tourism

This study aims to use sentiment analysis with a Textblob package for a tourism business case study in Thailand to stimulate the tourism economy post-COVID-19. Sentiment analysis is a text mining technique in a text processing field which is has been an interesting research topic for many years. It is applied to gather tourism information from travellers’ opinions, sentiments, emotions expressed, and attitudes in written language on social media such as review postings, forums, Twitter, blogs, and Facebook.   This information is very useful to improve the service of the tourism business. We evaluate performance with Python's TextBlob package with the TripAdvisor dataset from Kaggle that contains 10,000 travellers’ records. Thus, the Native Bayes model in the library is used to analyze tourism sentiments by default. The experimental results show Python's TextBlob package provides 89.32% accuracy which can vary depending on the training data and its suitability to implement for the text mining process in the tourism application.
AdaptText: A Novel Framework for Domain-Independent Automated Sinhala Text Classification

Sinhala language is being the widely used language in Sri Lanka. With the advancement of internet usage in Sri Lanka, an incredible amount of Sinhala text data is being added to the internet. In order to manage, analyze and make decisions from the available text data, it requires text classification. Being a low resource and morphologically rich language requires higher expertise and a considerable amount of budget and time to develop an effective task-specific text classifier. This research aims to develop a domain or dataset agnostic and automated solution to improve the quality and address current research gaps of text classification in Sinhala. Based on the solution, a high-level development framework and a user interface are developed. In addition, we perform a cross-domain evaluation with multiple datasets to evaluate the effectiveness of the solution. The proposed framework achieved state-of-the-art results for the Sinhala text classification.
Learning Knowledge Graph-based World Models of Textual Environments

World models improve a learning agent's ability to efficiently operate in interactive and situated environments. This work focuses on the task of building world models of text-based game environments. Text-based games, or interactive narratives, are reinforcement learning environments in which agents perceive and interact with the world using textual natural language. These environments contain long, multi-step puzzles or quests woven through a world that is filled with hundreds of characters, locations, and objects. Our world model learns to simultaneously: (1) predict changes in the world caused by an agent's actions when representing the world as a knowledge graph; and (2) generate the set of contextually relevant natural language actions required to operate in the world. We frame this task as a Set of Sequences generation problem by exploiting the inherent structure of knowledge graphs and actions and introduce both a transformer-based multi-task architecture and a loss function to train it. A zero-shot ablation study on never-before-seen textual worlds shows that our methodology significantly outperforms existing textual world modeling techniques as well as the importance of each of our contributions.
Examining the Prediction of Contact-Tracing App Adoption Using an Integrated Theoretical Model and Hybrid Approach Analysis

Covid-19 contact-tracing applications (CTAs) offer enormous potential to mitigate the surge of positive coronavirus cases, thus helping stakeholders to monitor high-risk areas. This study hypothesized an integrated model which comprises the technology acceptance model (TAM), privacy calculus theory (PCT), and task-technology fit (TFF) model. The hypothesized is use to better understand behavioral intention towards using the Tawakkalna mobile CTA. This study performed structural equation modeling (SEM) analysis as well as artificial neural network (ANN) analysis to validate the model, using survey data from 309 users of CTAs in the Kingdom of Saudi Arabia (KSA). The findings revealed that perceived ease of use and usefulness has positively and significantly impacted behavioral intention of Tawakkalna mobile CTA. Similarly, task features and mobility positively and significantly influence task–technology fit, and significantly affecting the behavioral intention of the CTA. However, the privacy risk, social concerns, and perceived benefits of social interaction are not significant factors. The findings provide adequate knowledge of the relative impact of key predictors of the behavioral intention of the Tawakkalna contact-tracing app.
We've had this conversation before: A Novel Approach to Measuring Dialog Similarity

Dialog is a core building block of human natural language interactions. It contains multi-party utterances used to convey information from one party to another in a dynamic and evolving manner. The ability to compare dialogs is beneficial in many real world use cases, such as conversation analytics for contact center calls and virtual agent design. We propose a novel adaptation of the edit distance metric to the scenario of dialog similarity. Our approach takes into account various conversation aspects such as utterance semantics, conversation flow, and the participants. We evaluate this new approach and compare it to existing document similarity measures on two publicly available datasets. The results demonstrate that our method outperforms the other approaches in capturing dialog flow, and is better aligned with the human perception of conversation similarity.
An adaptive dimension reduction algorithm for latent variables of  variational autoencoder

Constructed by the neural network, variational autoencoder has the overfitting problem caused by setting too many neural units, we develop an adaptive dimension reduction algorithm that can automatically learn the dimension of latent variable vector, moreover, the dimension of every hidden layer. This approach not only apply to the variational autoencoder but also other variants like Conditional VAE(CVAE), and we show the empirical results on six data sets which presents the universality and efficiency of this algorithm. The key advantages of this algorithm is that it can converge the dimension of latent variable vector which approximates the dimension reaches minimum loss of variational autoencoder(VAE), also speeds up the generating and computing speed by reducing the neural units.
Studi pengaruh variasi fraksi volum filler terhadap sifat mekanik komposit matriks polimer (PMC) berpenguat cangkang Kerang Hijau (Perna Viridis L.)

A polymer matrix composite has been made to strengthen green mussel shell particles (Perna Viridis L.) as an effort to utilize the potential of green mussel waste for polymer composite filler material. The purpose of this study was to determine the effect of the volume fraction of the green mussel shell particles on the mechanical properties of polymer matrix composites. The mechanical properties tested are the tensile strength and impact strength of the Polymer Matrix Composite material. The matrix material used in this research is polyester Yucalak157 @ BQTN-EX with Methyl Ethyl Keton Peroxide as catalyst and filler material in the form of 100 mesh green mussel shell particles with a variation of the filler volume fraction of 10%, 20%, 30% and 40%. This composite manufacturing method is to use the hand lay up method. The results of this study, obtained tensile strength values between 32.81-39.77 MPa where the highest tensile strength was found in specimens with a filler volume fraction of 40% and the lowest in specimens with a filler volume fraction of 10%; and impact strength (ductility) between 0.190-0.317 J / mm2 where the highest impact value was found in the test sample with a filler volume fraction of 10% and the lowest was in the specimen with a filler volume fraction of 40%. The increase in tensile strength value is influenced by the addition of a larger filler composition. On the other hand, the durability / impact price decreases with the increase in filler given. Based on microstructural observations, the greater the filler composition added to the matrix can reduce the size of the voids formed in the composites that have been made, it can be interpreted that the distribution of fillers is more evenly distributed.
Out-of-Distribution Generalization in Kernel Regression

In real word applications, data generating process for training a machine learning model often differs from what the model encounters in the test stage. Understanding how and whether machine learning models generalize under such distributional shifts have been a theoretical challenge. Here, we study generalization in kernel regression when the training and test distributions are different using methods from statistical physics. Using the replica method, we derive an analytical formula for the out-of-distribution generalization error applicable to any kernel and real datasets. We identify an overlap matrix that quantifies the mismatch between distributions for a given kernel as a key determinant of generalization performance under distribution shift. Using our analytical expressions we elucidate various generalization phenomena including possible improvement in generalization when there is a mismatch. We develop procedures for optimizing training and test distributions for a given data budget to find best and worst case generalizations under the shift. We present applications of our theory to real and synthetic datasets and for many kernels. We compare results of our theory applied to Neural Tangent Kernel with simulations of wide networks and show agreement. We analyze linear regression in further depth.
THE CORRELATION BETWEEN LISTENING TO ENGLISHSONGS AND STUDENTS’ VOCABULARY MASTERY OF THESECOND GRADE STUDENTS AT SMPN 01SUMBERGEMPOL IN THE ACADEMIC YEAR 2019/2020

ABSTRACTYuliani, Lailatul Maulida. Student Registered Number. 17203163214. TheCorrelation Between Listening to English Songs and Students’Vocabulary Mastery of the Second Grade Students at SMPN 01Sumbergempol in the Academic Year 2019/2020. Thesis. TadrisEnglish Education Department. Faculty of Tarbiyah and TeacherTraining. State Islamic Institute (IAIN) of Tulungagung. Advisor: Dr.Nurul Chojimah, M.PdKeyword: Vocabulary Mastery, Listening Activity, English Song.Listening English song is one method for learning English, especially forlearning vocabulary. In a school students often feel that learning is very boring.Students need other learning alternatives that are more fun. So that, students havemore enthusiasm for learning and it will influence their achievements invocabulary mastery. In this research, the researcher examined a correlationbetween listening English songs and students’ vocabulary mastery of the secondgrade students’ of SMPN 01 Sumbergempol. The aim of the research was to findout whether or not there is any significant correlation between listening Englishsong and students’ vocabulary mastery.The formulation of the research problem of this study is: Is there anysignificant correlation between listening English songs and students’ vocabulary?.The aim of this study is: To find out whether there is or not any correlation oflistening to English songs and students’ vocabulary mastery.This research uses quantitative approach with a correlational design usingtwo variables. In this research, the researcher examined the relationship betweentwo variables: one predictor variable and one criterion variable. The instrumentsof this research is questionnaire and test. The questionnaire is used to find the datascores of listening English song. The test is used to find data scores of vocabularymastery. The subject of this study are 51 students of second grade at SMPN 01Sumbergempol.The findings of this study showed a positive correlation betweentwo variables because the result of the correlation coefficient computation is0.457. Based on the calculating correlation coefficient, this research has moderatecorrelation, because the correlation coefficient is 0.457. Analysis of data throughcalculating using Pearson Product Moment Formula by SPSS 16.0 for windows,the data indicated that the sig (2-tailed) value 0.001 is lower than level ofsignificant (ɑ) 1 % it means that the null hypothesis (H0) is rejected andautomatically the alternative hypothesis (H1) is accepted. It can be conclude thatthere is significant correlation between listening English song and students’vocabulary mastery of second grade students at SMPN 01 Sumbergempol.
LibGuides: Introduction to OpenRefine: Getting Started

OpenRefine is free tool used for cleaning, editing, and transforming data sets into other formats. Orginally developed by Google (as Google Refine), OpenRefine is now supported by the opensource community. What is OpenRefine and how can you use it.
INFLUENCE OF ACTIVE LEARNING STRATEGY TO IMPROVE SPEAKING SKILL

The objective of this study is to see the effect of Active Learning Strategy on the students’ speaking skill in English teaching of the eighth grade students of SMP Negeri 6 Palu. It employed a pre-experimental research design, that is, one group pre-test and post-test design. The population  of this research is the eighthgrade students of SMP Negeri 6 Palu. The sample of this research is selected by using purposive sampling technique in which class VIIIB consists of 26 students as the subject of this research. The instrument of collecting the data is test (pre-test and post-test). The data are then analyzed by using t-test formula. The result of the test shows that the mean score of post-test (71.81) is greater than the pre-test (50.43). The data shows that the t-counted (14.6) is greater than t-table (1.708). The data indicates that there is a significant improvement of the Active Learning Strategy towards the students’ speaking skill of the eighth grade students of SMP Negeri 6 Palu. Keywords: Speaking Skill; Active Learning Strategy, Fluency, Accuracy and Comprehensibility.
On the Expressivity of Markov Reward

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of that might be desirable: (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists. We conclude with an empirical study that corroborates and illustrates our theoretical findings.
Use and effectiveness of Digital Contact Tracing during COVID-19 pandemic: a systematic review

Abstract Background Contact tracing (CT) is a pillar of infection control during outbreaks and its implementation has been great ally in the context of the COVID-19 pandemic that pressured national health systems. Digital CT (DCT) aids manual CT by logging proximity contacts and timely notifying contacts of positive cases, contributing to greater epidemic. We aim to systematically review and critically appraise the available evidence on adoption, effectiveness, acceptance and barriers to access of available CT apps. Methods We conducted a systematic literature review by means of Pubmed, Embase, Cochrane and Web of Science and applying the PRISMA guidelines. We classified studies according to selected characteristics (eg, original data, mathematical models, policy papers, overview of the experience with national CT apps) in order to better outline our results. Results Our queries yielded 1201 articles, of which 790 remained after duplicates' removal. After applying a priori selected inclusion criteria, 428 (54.17%) were eliminated after screening by title and abstract. 362 records were included in our study: of them 33.7% were original studies, 54.1% were opinion papers not reporting quantitative data, 14.91% were mathematical models, 6% were systematic reviews on DCT-related topics. Among original studies, 11.47% described national experiences and 25.40% discussed adoption dynamics. Conclusions DCT adoption still faces several cultural, normative and technical barriers, ranging from adoption mandates to privacy issues, to device compatibility. Overall, scant evidence is available on the impact of DCT in controlling infection spread and wide heterogeneity persists within studies in term of design and assessed outcomes Future research is needed based on shared evaluation frameworks. Key messages Digital contact tracing apps are a powerful but underestimated tool to counter epidemiological emergencies. Future epidemiological challenges will require a shared implementation and evaluation protocol for digital contact tracing apps.
Volume Rendering of Neural Implicit Surfaces

Neural volume rendering became increasingly popular recently due to its success in synthesizing novel views of a scene from a sparse set of input images. So far, the geometry learned by neural volume rendering techniques was modeled using a generic density function. Furthermore, the geometry itself was extracted using an arbitrary level set of the density function leading to a noisy, often low fidelity reconstruction. The goal of this paper is to improve geometry representation and reconstruction in neural volume rendering. We achieve that by modeling the volume density as a function of the geometry. This is in contrast to previous work modeling the geometry as a function of the volume density. In more detail, we define the volume density function as Laplace's cumulative distribution function (CDF) applied to a signed distance function (SDF) representation. This simple density representation has three benefits: (i) it provides a useful inductive bias to the geometry learned in the neural volume rendering process; (ii) it facilitates a bound on the opacity approximation error, leading to an accurate sampling of the viewing ray. Accurate sampling is important to provide a precise coupling of geometry and radiance; and (iii) it allows efficient unsupervised disentanglement of shape and appearance in volume rendering. Applying this new density representation to challenging scene multiview datasets produced high quality geometry reconstructions, outperforming relevant baselines. Furthermore, switching shape and appearance between scenes is possible due to the disentanglement of the two.