Reviewerly/exHarmony huggingface dataset:
TempLe: Learning Template of Transitions for Sample Efficient Multi-task RL

Transferring knowledge among various environments is important for efficiently learning multiple tasks online. Most existing methods directly use the previously learned models or previously learned optimal policies to learn new tasks. However, these methods may be inefficient when the underlying models or optimal policies are substantially different across tasks. In this paper, we propose Template Learning (TempLe), a PAC-MDP method for multi-task reinforcement learning that could be applied to tasks with varying state/action space without prior knowledge of inter-task mappings. TempLe gains sample efficiency by extracting similarities of the transition dynamics across tasks even when their underlying models or optimal policies have limited commonalities. We present two algorithms for an ``online'' and a ``finite-model'' setting respectively. We prove that our proposed TempLe algorithms achieve much lower sample complexity than single-task learners or state-of-the-art multi-task methods. We show via systematically designed experiments that our TempLe method universally outperforms the state-of-the-art multi-task methods (PAC-MDP or not) in various settings and regimes.
HIT - A Hierarchically Fused Deep Attention Network for Robust Code-mixed Language Representation

Understanding linguistics and morphology of resource-scarce code-mixed texts remains a key challenge in text processing.Although word embedding comes in handy to support downstream tasks for low-resource languages, there are plenty of scopes in improving the quality of language representation particularly for code-mixed languages.In this paper, we propose HIT, a robust representation learning method for code-mixed texts.HIT is a hierarchical transformer-based framework that captures the semantic relationship among words and hierarchically learns the sentencelevel semantics using a fused attention mechanism.HIT incorporates two attention modules, a multi-headed self-attention and an outer product attention module, and computes their weighted sum to obtain the attention weights.Our evaluation of HIT on one European (Spanish) and five Indic (Hindi, Bengali, Tamil, Telugu, and Malayalam) languages across four NLP tasks on eleven datasets suggests significant performance improvement against various state-of-the-art systems.We further show the adaptability of learned representation across tasks in a transfer learning setup (with and without fine-tuning).FAME Add, Norm, FFN HIT encoder (1) HIT encoder (l c )
Modeling Task-Aware MIMO Cardinality for Efficient Multilingual Neural Machine Translation

Neural machine translation has achieved great success in bilingual settings, as well as in multilingual settings.With the increase of the number of languages, multilingual systems tend to underperform their bilingual counterparts.Model capacity has been found crucial for massively multilingual NMT to support language pairs with varying typological characteristics.Previous work increases the modeling capacity by deepening or widening the Transformer.However, modeling cardinality based on aggregating a set of transformations with the same topology has been proven more effective than going deeper or wider when increasing capacity.In this paper, we propose to efficiently increase the capacity for multilingual NMT by increasing the cardinality.Unlike previous work which feeds the same input to several transformations and merges their outputs into one, we present a Multi-Input-Multi-Output (MIMO) architecture that allows each transformation of the block to have its own input.We also present a task-aware attention mechanism to learn to selectively utilize individual transformations from a set of transformations for different translation directions.Our model surpasses previous work and establishes a new state-of-the-art on the large scale OPUS-100 corpus while being 1.31 times as fast.
Perencanaan Strategis Sistem Informasi Di Sinode GKJ Menggunakan Enterprise Architecture Planning Framework

The GKJ Synod is one of the Church Foundations located in Salatiga which is an institution that focuses on the field of information data center and media services for the GKJ Synod. The Salatiga GKJ Synod currently has a system but still has problems with Information System problems that have not been implemented optimally, so there are often obstacles when doing work. Because it does not run quickly and well, therefore an Information System Strategic Planning is needed, this research aims to be able to help the business processes contained in the GKJ Synod implement information systems in the organization to run well. The purpose of this research is to propose and plan using the method Enterprise Architecture Planning. Enterprise Architecture Planning (EAP) is the process of defining the architecture in the use of information systems to support the architecture implementation plan. By using the approach, the Enterprise Architecture Planning (EAP)method can help the GKJ Salatiga synod to plan data quality oriented to business needs in order to achieve and support business goals for agencies and organizations, besides that, the advantages of using Enterprise Architecture Planning are a supporting method for making decisions and good planning for an organization.
On Predicting Personal Values of Social Media Users using Community-Specific Language Features and Personal Value Correlation

Personal values have significant influence on individuals' behaviors, preferences, and decision making. It is therefore not a surprise that personal values of a person could influence his or her social media content and activities. Instead of getting users to complete personal value questionnaire, researchers have looked into a non-intrusive and highly scalable approach to predict personal values using user-generated social media data. Nevertheless, geographical differences in word usage and profile information are issues to be addressed when designing such prediction models. In this work, we focus on analyzing Singapore users' personal values, and developing effective models to predict their personal values using their Facebook data. These models leverage on word categories in Linguistic Inquiry and Word Count (LIWC) and correlations among personal values. The LIWC word categories are adapted to non-English word use in Singapore. We incorporate the correlations among personal values into our proposed Stack Model consisting of a task-specific layer of base models and a cross stitch layer model. Through experiments, we show that our proposed model predicts personal values with considerable improvement of accuracy over the previous works. Moreover, we use the stack model to predict the personal values of a large community of Twitter users using their public tweet content and empirically derive several interesting findings about their online behavior consistent with earlier findings in the social science and social media literature.
Co-evolution of Metamodel and Generators: Higher-order Templating to the Rescue.

In Model-Driven Engineering, metamodels are the cornerstone entities underpinning modeling environments.Given one or more metamodels, a diversity of artefacts, including models, transformations, and code generators, are formally coupled with them.Like any other software, metamodels are prone to evolutionary pressure due to shifting business requirements.However, metamodel modifications might come at the price of jeopardizing the related artefacts that, in turn, must be adapted in order to remain valid.While a comprehensive corpus of research has shown that the co-evolution of metamodels and models can be effectively addressed with semi-automated techniques, the co-evolution of transformations and code generators still requires significant manual intervention.This paper proposes a novel technique to make template-based code generators resilient, to some extent, against metamodel evolution.A new template-based language, called hotello, is proposed for the specification of meta-templates, i.e., higher-order model-to-text transformations.The approach has been implemented, and a demonstration of its capabilities on a case study in the IoT domain is discussed.
Analogy Training Multilingual Encoders

Language encoders encode words and phrases in ways that capture their local semantic relatedness, but are known to be globally inconsistent. Global inconsistency can seemingly be corrected for, in part, by leveraging signals from knowledge bases, but previous results are partial and limited to monolingual English encoders. We extract a large-scale multilingual, multi-word analogy dataset from Wikidata for diagnosing and correcting for global inconsistencies, and then implement a four-way Siamese BERT architecture for grounding multilingual BERT (mBERT) in Wikidata through analogy training. We show that analogy training not only improves the global consistency of mBERT, as well as the isomorphism of language-specific subspaces, but also leads to consistent gains on downstream tasks such as bilingual dictionary induction and sentence retrieval.
A study of similar question retrieval method in online health communities

Purpose The purpose of this paper is to propose a new approach to retrieve similar questions in online health communities to improve the efficiency of health information retrieval and sharing. Design/methodology/approach This paper proposes a hybrid approach to combining domain knowledge similarity and topic similarity to retrieve similar questions in online health communities. The domain knowledge similarity can evaluate the domain distance between different questions. And the topic similarity measures questions’ relationship base on the extracted latent topics. Findings The experiment results show that the proposed method outperforms the baseline methods. Originality/value This method conquers the problem of word mismatch and considers the named entities included in questions, which most of existing studies did not.
How to Split: the Effect of Word Segmentation on Gender Bias in Speech Translation

Having recognized gender bias as a major issue affecting current translation technologies, researchers have primarily attempted to mitigate it by working on the data front.However, whether algorithmic aspects concur to exacerbate unwanted outputs remains so far under-investigated.In this work, we bring the analysis on gender bias in automatic translation onto a seemingly neutral yet critical component: word segmentation.Can segmenting methods influence the ability to translate gender?Do certain segmentation approaches penalize the representation of feminine linguistic markings?We address these questions by comparing 5 existing segmentation strategies on the target side of speech translation systems.Our results on two language pairs (English-Italian/French) show that state-of-the-art subword splitting (BPE) comes at the cost of higher gender bias.In light of this finding, we propose a combined approach that preserves BPE overall translation quality, while leveraging the higher ability of character-based segmentation to properly translate gender.
The Many Faces of Instruction: An Exploration of Academic Librarians’ Teaching Personas

While several studies explore whether librarians think of themselves as teachers, how librarians construct their teacher identities has received less attention in the literature.This project used semi-structured interviews with eighteen academic librarians in the United States to gain a sense of their teaching personas and how these have developed and evolved over time.The participants valued authenticity but were also able to quickly adapt their personas to different contexts.Librarians wish to be seen as friendly experts and develop their values-based teaching personas slowly over the course of their careers.The results of this study can help shape professional development efforts aimed at librarians who teach as well as provide guidance to library and information science students as they learn about information literacy instruction.
SyGNS: A Systematic Generalization Testbed Based on Natural Language Semantics

Recently, deep neural networks (DNNs) have achieved great success in semantically challenging NLP tasks, yet it remains unclear whether DNN models can capture compositional meanings, those aspects of meaning that have been long studied in formal semantics.To investigate this issue, we propose a Systematic Generalization testbed based on Natural language Semantics (SyGNS), whose challenge is to map natural language sentences to multiple forms of scoped meaning representations, designed to account for various semantic phenomena.Using SyGNS, we test whether neural networks can systematically parse sentences involving novel combinations of logical expressions such as quantifiers and negation.Experiments show that Transformer and GRU models can generalize to unseen combinations of quantifiers, negations, and modifiers that are similar to given training instances in form, but not to the others.We also find that the generalization performance to unseen combinations is better when the form of meaning representations is simpler.The data and code for SyGNS are publicly available at https: //github.com/verypluming/SyGNS.
IFlyEA: A Chinese Essay Assessment System with Automated Rating, Review Generation, and Recommendation

Automated Essay Assessment (AEA) aims to judge students' writing proficiency in an automatic way.This paper presents a Chinese AEA system IFlyEssayAssess (IFlyEA), targeting on evaluating essays written by native Chinese students from primary and junior schools.IFlyEA provides multi-level and multi-dimension analytical modules for essay assessment.It has state-of-the-art grammar level analysis techniques, and also integrates components for rhetoric and discourse level analysis, which are important for evaluating native speakers' writing ability, but still challenging and less studied in previous work.Based on the comprehensive analysis, IFlyEA provides application services for essay scoring, review generation, recommendation, and explainable analytical visualization.These services can benefit both teachers and students during the process of writing teaching and learning.
Online and blended learning in vocational training institutions in South Korea

This study aims to explore the current status of using e-learning in vocational training institutions in South Korea and the institutions’ intent to adopt e-learning or blended learning. A total of 116 responses to an online survey were received from the instructors and administrators in vocational training institutions. The findings of the study are summarized as follows. First, e-learning content was found mostly used as supplementary materials in blended or classroom learning. Second, the main reasons for not using e-learning were related to the low training effectiveness in e-learning and the difficulty of finding appropriate e-learning content. Third, the dominant reasons that vocational training institutions believed they might need e-learning were related to content reuse and e-learning flexibility. Fourth, National Competency Standards-based content, theory-focused content, and practice alternative content were found to be most useful e-learning content in vocational training institutions. Fifth, the most preferred type of e-learning content was smaller modules developed for learning specific knowledge or skills. Lastly, vocational training institutions were found to be hesitant about using e-learning in the future because of the lower training effectiveness in e-learning and the lack of interaction among learners and instructors in e-learning contexts. Suggestions for practitioners and researchers are provided on the basis of the findings.
Integration of Museum User Behavior Information Based on Wireless Network

Online museum information resource systems are getting popular these days which allow the users to get detailed information about the objects of their interest, and the user preferences are stored to search for related artifacts considering his/her online behavior. The behavior of users browsing online is integrated to capture relevant information which is integrated into museum information resources. Unfortunately, present implementations have errors in integration and optimization system, so a wireless network-based museum user behavior information integration system is proposed to calculate the user’s interest in museum’s cultural relics. The user behavior information resource model is developed based upon the degree of user interest, and forgetting functions with different decay rates are employed to describe changes in the interest level. This information is then used to construct users’ interest matrices. This matrix also contains information regarding the cultural relics that users have not yet visited. The system will introduce the interest weights of feature words to take the top features of the user behavior information for the integration of the users’ behavior and to combine the feature vectors that can represent the overall trajectory. Moreover, those feature vectors are described that can represent the local trajectory into feature vector to identify the slow-moving sparse targets, which is then utilized for the integration of users’ behavior information. The simulation tests prove that the proposed method can achieve low error in the integration process of user behavior information resources, thereby yielding good results.
Optical Inspection Systems for Axisymmetric Parts with Spatial 2D Resolution

The article proposes a solution to the problem of increasing the accuracy of determining the main shaping dimensions of axisymmetric parts through a control system that implements the optical method of spatial resolution. The influence of the projection error of a passive optical system for controlling the geometric parameters of bodies of revolution from the image of its sections, obtained by a digital camera with non-telecentric optics, on the measurement accuracy is shown. Analytical dependencies are derived that describe the features of the transmission of measuring information of a system with non-telecentric optics in order to estimate the projection error. On the basis of the obtained dependences, a method for compensating the projection error of the systems for controlling the geometry of the main shaping surfaces of bodies of revolution has been developed, which makes it possible to increase the accuracy of determining dimensions when using digital cameras with a resolution of 5 megapixels or more, equipped with short-focus lenses. The possibility of implementing the proposed technique is confirmed by the results of experimental studies.
Read Aloud Video Sebagai Media Pembelajaran Daring pada Masa Pandemi Covid-19 di TK Aisyiyah Sidoharjo - Wonogiri

Besides writing skill, reading skill is one of the students' basic competencies at the kindergarten level. During online learning, due to the Covid-19 pandemic, teachers should to create the right learning media; thus, the students still have this competency with the assistance of parents at home. However, based on observations, teachers at Aisyiyah Kindergarten apply a monotonous learning mechanism for all materials, including students' reading mastery competence, which was limited to assignments in WhatsApp messenger in a virtual class and no home visit activities due to unsupportive circumstances. This education-based community service aims to help teachers provide learning media for reading topics through read-aloud videos uploaded by Youtube. In this video, we told the contents of the Fable genre book. The learning process was carried out by ourselves with the class teacher's permission in the WhatsApp group. The video link of Youtube was shared with ten parents in the WhatsApp Group, then students, accompanied by their parents, listened to the Read Aloud video. Ten students who successfully sent videos about retelling the story's contents in the WhatsApp group will be given a poster card as a reward. The results showed that students liked to read aloud videos, helped increase concentration and understanding of the book's contents (in this case, the stories in a read-aloud video), self-confidence, how to communicate verbally as well, and encouraged students' motivation in online learning. By using our read-aloud video, teachers can be helped to provide proper learning media to the children. Later, online learning at Aisyiyah Kindergarten can be more varied.
EAFS: An Efficient, Accurate, and Forward Secure Searchable Encryption Scheme Supporting Range Search

Forward privacy in existing searchable symmetric encryption approaches generally supports only keyword searches, rather than range searches. In addition, existing approaches that support range search over encrypted datasets have limitations associated with efficiency, accuracy, and security. Seeking to address these limitations, we formally propose the definition of the forward privacy for secure range searches. Also, we demonstrate how three widely used cryptographic tools— order-preserving encryption (OPE), pseudorandom function, and one-time pad–can be leveraged to design an efficient, accurate, and forward secure (EAFS) searchable encryption scheme supporting range search in encrypted numerical databases. In the proposed EAFS scheme, a trapdoor only matches the last data record that satisfies the search range, and the other results are found iteratively using the previous result. The chain-like search and the embedded ciphertexts of OPE simultaneously guarantee its efficiency, forward privacy, and accuracy. We also use the simulation-based method to demonstrate that our scheme is secure. Then, we implement EAFS in Microsoft Azure and evaluate its performance. The evaluation findings demonstrate that our proposed scheme supports secure range search practically and efficiently.
Sophisticated analysis of a method to eliminate fruitless cycles for Pollard's rho method with skew Frobenius mapping over a Barreto-Naehrig curve

In this paper, the authors focus on and propose an approach to attack a kind of pairing-friendly curves, the Barreto-Naehring (BN) curve, to accelerate the evaluation of the security level concerning the elliptic curve discrete logarithm problem (ECDLP). More precisely, this paper targets the BN curve, which is known to be a pairing-friendly curve, and Pollard's rho method based on the random-walk is adopted to attack the curve. Though Pollard's rho method with skew Frobenius mapping is known to solve the ECDLP efficiently, this approach sometimes induces the unsolvable cycle, called the fruitless cycle, and such trials must restart with a different starting point. However, any effective method to eliminate such fruitless cycles has not been proposed. Therefore, the authors focus and give the sophisticated analysis to propose an effective approach to eliminate such cycles to optimize Pollard's rho method furthermore. In addition, we confirm the effectiveness of the method by applying it to a BN curve with 12, 17, and 33-bit parameters.
APRIORI ALGORITHM FOR DETERMINING THE DEMAND LEVEL OF STATIONARY PT. MAIN GAFA INDONESIA

PT Gafa Utama Indonesia is one company that provides services in teaching and writing. Until now, PT Gafa Utama Indonesia already has 30 well-known branches in Jabodetabek. In the teaching and learning process, Gafa needs some stationery and teaching aids. The high demand for office stationery, and the mismatch of inventory in the warehouse, affects the fluency in the teaching and learning process. The data used in this study is the report data on the demand for office stationery for the period January-December 2018. This study uses a priori algorithm method and assessment with tanagra tools. The results of manual calculations with Microsoft Excel are the same as those using the tanagra tool. The final result shows the 2 items with the most demand, namely an eraser and a sharpener with at least 50% support, and 50% confidence. These results can be used as a reference for PT Gafa Utama Indonesia in the supply of office stationery