Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploration Study of Factors Caused Out of School Rate, Impact, and Strategies to Overcome Them

The aims of the research was to explore the causes, impact, and strategies of students’ dropout in the primary school in Kebumen District. This research is a mixed methods research. The sampling technique used purposive sampling technique with a total sample of 144 students dropping out of school. Data collection techniques used questionnaires, observations, interviews, focus group discussions (FGD), and documentation. The results of the study show that: 1) The most dominant factors in influencing children dropping out of school are economic factors; 2) These factors are; economic status, low thinking ability, broken home, culture, and zonasi system; 3) The impact that occurs when children drop out of school is difficulty finding work, entering the "Kid Punk" group, getting married at a young age and getting a low family income, increasing unemployment, and disturbing the community; 4) These strategy to deal with dropouts is to add places and non-formal education managers to pursue package A in remote locations/villages, add inclusion of schools, especially for regions remote areas, a zoning system for new student admissions in order to prioritize the environment closest to the education unit, and the involvement of parents and the community in the importance of children's education
SMAS: a smart alert system for localization and first response to fires on ro-ro vessels

In this paper we present the design and utility of a Smart Alert System (SMAS) for quick first response and effective fighting of fires in their initial stages on roll-on / roll-off (ro-ro) vessels. Given that localization within the steel structures of a vessel is currently an open problem, we develop a ground-breaking infrastructure-free (i.e., "zero" infrastructure) localization architecture to localize on ordinary smartphones in vessel indoor spaces that lack any infrastructure whatsoever (e.g., Wi-Fi, BLE, UWB, RFID, LED). Particularly, we developed a smartphone-based Computer Vision (CV) localization technology upon which an innovative nearest neighbor information communication channel for spatio-textual alerting between first responders is constructed. We also develop information sharing channels of multimedia content (e.g., heat scans) but also provide an integrated search and navigation tool for stationary and mobile assets of a vessel and the fire control operation. We have developed a complete functional system of SMAS using a micro-service edge architecture that deploys sharded sqlite micro-databases. We will present SMAS in two modes: (i) Online Mode, where attendees will be able to carry out simulated emergency scenarios; and (ii) Offline Mode, where attendees will be able to observe emergency scenarios recorded on video traces.
Detecting resource utilization bugs induced by variant lifecycles in Android

The lifecycle models of Android components such as Activities and Fragments predefine the possible orders in which the components' callback methods will be invoked during app executions. Correspondingly, resource utilization operations performed by Android components must comply with all possible lifecycles to ensure safe utilization of the resources in all circumstances, which, however, can be challenging to achieve. In response to the challenge, various techniques have been developed to detect resource utilization bugs that manifest themselves when components go through common lifecycles, but the fact that Android components may execute their callback methods in uncommon orders, leading to variant component lifecycles, has largely been overlooked by the existing techniques. In this paper, we first identify three variant lifecycles for Android Activities and Fragments and then develop a technique called VALA to automatically detect bugs in Android apps that are induced by the variant lifecycles and may cause resource utilization errors like resource leaks and data losses. In an experimental evaluation conducted on 35 Android apps, a supporting tool for the VALA technique automatically detected 8 resource utilization bugs. All the 8 bugs were manually confirmed to be real defects and 7 of them were reported for the first time.
TeLL: log level suggestions via modeling multi-level code block information

Developers insert logging statements into source code to monitor system execution, which forms the basis for software debugging and maintenance. For distinguishing diverse runtime information, each software log is assigned with a separate verbosity level (e.g., trace and error). However, choosing an appropriate verbosity level is a challenging and error-prone task due to the lack of specifications for log level usages. Prior solutions aim to suggest log levels based on the code block in which a logging statement resides (i.e., intra-block features). Such suggestions, however, do not consider information from surrounding blocks (i.e., inter-block features), which also plays an important role in revealing logging characteristics. To address this issue, we combine multiple levels of code block information (i.e., intra-block and inter-block features) into a joint graph structure called Flow of Abstract Syntax Tree (FAST). To explicitly exploit multi-level block features, we design a new neural architecture, Hierarchical Block Graph Network (HBGN), on the FAST. In particular, it leverages graph neural networks to encode both the intra-block and inter-block features into code block representations and guide log level suggestions. We implement a prototype system, TeLL, and evaluate its effectiveness on nine large-scale software systems. Experimental results showcase TeLL's advantage in predicting log levels over the state-of-the-art approaches.
Substream management in distributed streaming dataflows

Most state-of-the-art SPEs use punctuations to divide a stream into bounded substreams of messages, such as epochs and windows. The punctuation approach is powerful but has limitations: it does not support cyclic dataflows, is poorly scalable in some cases due to intensive use of broadcasts, and becomes inefficient when the number of chunks or cluster size becomes significant. We introduce a new substream tracking technique called trAcker that overcomes the limits of punctuations. We experimentally evaluate the properties of trAcker in both synthetic and real-world environments. Experiments show that our technique outperforms punctuations for a large number of substreams and efficiently handles real-world cyclic dataflows.
Evolution-aware detection of order-dependent flaky tests

Regression testing is an important part of the software development process but suffers from the presence of flaky tests. Flaky tests are tests that can nondeterministically pass or fail regardless of code changes. Order-dependent flaky tests are a prominent kind of flaky tests whose outcome depends on the test order in which they are run. Prior work has focused on detecting order-dependent flaky tests through rerunning all tests in different test orders on a single version of code. As code is constantly changing, rerunning all tests in different test orders after every change is costly. In this work, we propose IncIDFlakies, a technique to detect order-dependent flaky tests by analyzing code changes to detect newly-introduced order-dependent flaky tests due to those changes. Building upon existing work in iDFlakies that reruns tests in dif- ferent test orders, IncIDFlakies analyzes and selects to run only the tests that (1) are affected by the change, and (2) can potentially result in a test-order dependency among each other due to potential shared state. Running IncIDFlakies on 67 order-dependent flaky tests across changes in code in their respective projects, including the changes where they became flaky, we find that IncIDFlakies can select to run on average 65.4% of all the tests, resulting in running 68.4% of the time that baseline iDFlakies would use when running the same number of test orders with the full test suite. Furthermore, we find that IncIDFlakies can still ensure that the test orders it runs can potentially detect the order-dependent flaky tests.
Leisure’s relation to older adults adapting to new homes: A scoping review

Abstract As the global population ages, older adults may move to new homes, experiencing changes in their social and physical environments. Leisure has been linked to adjusting to these changes by promoting maintenance of identity and socialization. The purpose of this scoping review is to synthesize literature regarding leisure’s relation to older adults’ adaptation to new homes. Findings suggest leisure is linked to adaptation to new homes by providing a sense of continuity, belonging and connection. Additionally, the physical, institutional and social environments of the new home can facilitate or hinder leisure participation. This review adds to the discussion of leisure’s inter-relationships with sense of home and identity of older adults moving to new homes. This review suggests practitioners, residence planners and policy makers can develop strategies that help older adults maintain social networks and leisure opportunities following transitions into new environments. This review identified gaps and areas for future research.
ID10M: Idiom Identification in 10 Languages

Idioms are phrases which present a ﬁgurative meaning that cannot be (completely) derived by looking at the meaning of their individual components. Identifying and understanding idioms in context is a crucial goal and a key challenge in a wide range of Natural Language Understanding tasks. Although efforts have been undertaken in this direc-tion, the automatic identiﬁcation and understanding of idioms is still a largely under-investigated area, especially when operating in a multilingual scenario. In this paper, we address such limitations and put forward several new contributions: we propose a novel multilingual Transformer-based system for the identiﬁcation of idioms; we produce a high-quality automatically-created training dataset in 10 languages, along with a novel manually-curated evaluation benchmark; ﬁnally, we carry out a thorough performance analysis and release our evaluation suite at https:// .
Semantic-Preserving Abstractive Text Summarization with Siamese Generative Adversarial Net

We propose a novel siamese generative adver­ sarial net for abstractive text summarization (SSPGAN), which can preserve the main se­ mantics of the source text. Different from pre­ vious generative adversarial net based methods, SSPGAN is equipped with a siamese semantic-preserving discriminator, which can not only be trained to discriminate the machine-generated summaries from the human-summarized ones, but also ensure the semantic consistency be­ tween the source text and target summary. As a consequence of the min-max game between the generator and the siamese semantic-preserving discriminator, the generator can generate a sum­ mary that conveys the key content of the source text more accurately. Extensive experiments on several text summarization benchmarks in dif­ ferent languages demonstrate the effectiveness of the proposed method.
Improving Code-Switching Dependency Parsing with Semi-Supervised Auxiliary Tasks

Code-switching dependency parsing stands as a challenging task due to both the scarcity of necessary resources and the structural difficulties embedded in code-switched languages. In this study, we introduce novel sequence labeling models to be used as auxiliary tasks for dependency parsing of code-switched text in a semi-supervised scheme. We show that using auxiliary tasks enhances the performance of an LSTM-based dependency parsing model and leads to better results compared to an XLM-R-based model with significantly less computational and space complexity. As the first study that focuses on multiple code-switching language pairs for dependency parsing, we acquire state-of-the-art scores on all of the studied languages. Our best models outperform the previous work by 7.4 LAS points on average.
Pathway2Text: Dataset and Method for Biomedical Pathway Description Generation

Biomedical pathways have been extensively used to characterize the mechanism of complex diseases. One essential step in biomedical pathway analysis is to curate the description of a pathway based on its graph structure and node features. Neural text generation could be a plausible technique to circum-vent the tedious manual curation. In this paper, we propose a new dataset Pathway2Text, which contains 2,094 pairs of biomedical pathways and textual descriptions. All pathway graphs are experimentally derived or manually curated. All textual descriptions are written by domain experts. We form this problem as a Graph2Text task and propose a novel graph-based text generation approach k NN-Graph2Text, which explicitly exploited descriptions of similar graphs to generate new descriptions. We observed substantial improvement of our method on both Graph2Text and the reverse task of Text2Graph. We further illustrated how our dataset can be used as a novel benchmark for biomedical name entity recognition. Collectively, we envision our method will become an important benchmark for evaluating Graph2Text methods and advance biomedical research for complex diseases.
Heterogeneity of RNA editing in mesothelioma and how RNA editing enzyme ADAR2 affects mesothelioma cell growth, response to chemotherapy and tumor microenvironment

We previously observed increased levels of adenosine-deaminase-acting-on-dsRNA (Adar)-dependent RNA editing during mesothelioma development in mice exposed to asbestos. The aim of this study was to characterize and assess the role of ADAR-dependent RNA editing in mesothelioma. Tumors and mesothelioma primary cultures have higher ADAR-mediated RNA editing compared to mesothelial cells. Unsupervised clustering of editing in different genomic regions revealed heterogeneity between tumor samples as well as mesothelioma primary cultures. ADAR2 expression levels are higher in BRCA1-associated protein 1 wild-type tumors, with corresponding changes in RNA editing in transcripts and 3’UTR. ADAR2 knockdown and rescue models indicated a role in cell proliferation, altered cell cycle, increased sensitivity to antifolate treatment and type-1 interferon signaling upregulation, leading to changes in the microenvironment in vivo. Our data indicate that RNA editing contributes to mesothelioma heterogeneity and highlights an important role of ADAR2 not only in growth regulation in mesothelioma but also chemotherapy response, in addition to regulating inflammatory response downstream of sensing nucleic acid structures.
HUE: Pretrained Model and Dataset for Understanding Hanja Documents of Ancient Korea

Historical records in Korea before the 20th century were primarily written in Hanja, an extinct language based on Chinese characters and not understood by modern Korean or Chinese speakers. Historians with expertise in this time period have been analyzing the documents, but that process is very difficult and time-consuming, and language models would significantly speed up the process. Toward building and evaluating language models for Hanja, we release the Hanja Understanding Evaluation dataset consisting of chronological attribution, topic classification, named entity recognition, and summary retrieval tasks. We also present BERT-based models continued training on the two major corpora from the 14th to the 19th centuries: the Annals of the Joseon Dynasty and Diaries of the Royal Secretariats. We compare the models with several baselines on all tasks and show there are significant improvements gained by training on the two corpora. Additionally, we run zero-shot experiments on the Daily Records of the Royal Court and Important Officials (DRRI). The DRRI dataset has not been studied much by the historians, and not at all by the NLP community.
Anti-Overestimation Dialogue Policy Learning for Task-Completion Dialogue System

A dialogue policy module is an essential part of task-completion dialogue systems. Re-cently, increasing interest has focused on reinforcement learning (RL)-based dialogue policy. Its favorable performance and wise action decisions rely on an accurate estimation of action values. The overestimation problem is a widely known issue of RL since its estimate of the maximum action value is larger than the ground truth, which results in an unstable learning process and suboptimal policy. This problem is detrimental to RL-based dialogue policy learning. To mitigate this problem, this paper proposes a dynamic partial average estimator ( DPAV ) of the ground truth maximum action value. DPAV calculates the partial average between the predicted maximum action value and minimum action value, where the weights are dynamically adaptive and problem-dependent. We incorporate DPAV into a deep Q-network as the dialogue policy and. Our method can achieve better or comparable results compared to top baselines on three dialogue datasets of different domains with a lower computational load . In addition, we also theoretically prove the convergence and derive the upper and lower bounds of the bias compared with those of other methods.
Common-rail injector detection system of diesel car

The utility model provides a common-rail injector detection system of a diesel car. The common-rail injector detection system comprises an oil tank, an electric control high-pressure oil pump, a motor electrically connected with the electric control high-pressure oil pump, a common rail, an injector and a rail pressure control unit connected with the oil tank, the electric control high-pressure oil pump, the motor, the common rail and the injector respectively; an oil return pipe is connected between the electric control high-pressure oil pump and the oil tank; the common rail is communicated with the electric control high-pressure oil pump and the injector through a high-pressure oil pipe; the motor drives the electric control high-pressure oil pump to transfer the fuel oil into the common rail from the oil tank; an oil pump proportional valve is arranged between the common rail and the electric control high-pressure oil pump so as to control the oil mass entering the common rail; the common rail is also provided with a rail pressure sensor in electric connection with the rail pressure control unit; and one end of the injector is connected with the common rail, the oil tank and the electric control high-pressure oil pump through an oil pipe respectively. The common-rail injector detection system of a diesel engine automobile adopts a method of PI algorithm and D fuzzy control comprehensive algorithm, adapts to the adjustment requirements of different stages and ensures relatively good effect in stabilizing the rail pressure, and the pressure precision can reach +/-0.5 MPA.
Data Augmentation for Low-Resource Dialogue Summarization

We present DADS , a novel D ata A ugmentation 001 technique for low-resource D ialogue 002 S ummarization. Our method generates 003 synthetic examples by replacing sections of 004 text from both the input dialogue and summary 005 while preserving the augmented summary 006 to correspond to a viable summary for the 007 augmented dialogue. We utilize pretrained 008 language models that produce highly likely 009 dialogue alternatives while still being free to 010 generate diverse alternatives. We applied our 011 data augmentation method to the SAMSum 012 dataset in low resource scenarios, mimicking 013 real world problems such as chat, thread, and 014 meeting summarization where large scale 015 supervised datasets with human-written sum-016 maries are scarce. Through both automatic 017 and human evaluations, we show that DADS 018 shows strong improvements for low resource 019 scenarios while generating topically diverse 020 summaries without introducing additional 021 hallucinations to the summaries. 022
Continual Machine Reading Comprehension via Uncertainty-aware Fixed Memory and Adversarial Domain Adaptation

Continual Machine Reading Comprehension aims to incrementally learn from a continuous data stream across time without access the previous seen data, which is crucial for the development of real-world MRC systems. However, it is a great challenge to learn a new domain incrementally without catastrophically forgetting previous knowledge. In this paper, MA-MRC, a continual MRC model with uncertainty-aware fixed Memory and Adversarial domain adaptation, is proposed. In MA-MRC, a fixed size memory stores a small number of samples in previous domain data along with an uncertainty-aware updating strategy when new domain data arrives. For incremental learning, MA-MRC not only keeps a stable understanding by learning both memory and new domain data, but also makes full use of the domain adaptation relationship between them by adversarial learning strategy. The experimental results show that MA-MRC is superior to strong baselines and has a substantial incremental learning ability without catastrophically forgetting under two different continual MRC settings.
An n-ary zipWith in Haskell

The aim of this note is to present an alternative definition of the zipWith family in the Haskell Library Report [5]. Because of the difficulties in defining a well-typed function with a variable number of arguments, [5] presents a family of zipWith functions. It provides zip functions zipWith2, zipWith3, . , zipWith7. For each n, zipWithn zips n lists with a n-ary function. Defining a single zipWith function with a variable number of arguments seems to require dependent types. Inspired by [3], we show, however, how to define such a function in Haskell by means of a binary operator for grouping its arguments. For comparison, we also give definitions of zipWith in languages with dependent types.
National Bank of Ukraine Econometric Model for the Assessment of Banks’ Credit Risk and Support Vector Machine Alternative

Econometric models of credit scoring started with the introduction of Altman’s simple z-model in 1968, but since then these models have become more and more sophisticated, some even use Artificial Neural Networks (ANN) and Support Vector Machine (SVM) techniques. This paper focuses on the use of SVM as a model for default prediction. I start with an introduction to SVM as well as to some of its widespread alternatives. Then, these different techniques are used to model NBU data on banks’ clients, which allows us to compare the accuracy of SVM to the accuracy of other models. While SVM is generally more accurate, I discuss some of the features of SVM that make its practical implementation controversial. I then discuss some ways for overcoming those features. I also present the results of the Logistic Regression (Logit) model which will be used by the NBU.
Measuring the educators’ behavioural intention, perceived use and ease of use of mobile technologies

This research explores the educators’ attitudes and behavioural intention toward mobile applications. The methodology integrates measures from ‘the pace of technological innovativeness’ and the ‘technology acceptance model’ to understand the rationale for further investment in mobile learning (m-learning). It has investigated the educators’ stance toward ubiquitous learning resources, including mobile applications (apps) in schools. A principal component analysis has indicated that the educators were committed to using mobile technologies. In addition, a stepwise regression analysis has indicated the sociodemographic variables affected the educators’ perceived ease of use and usefulness of mobile technologies in classroom activities. This study has shown that that the younger female respondents were increasingly engaging in m-learning resources. In conclusion, this contribution puts forward key implications for both academia and practitioners.