Reviewerly/exHarmony huggingface dataset:
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation

QUESTEVAL is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.Its adaptation to Data-to-Text tasks is not straightforward as it requires multimodal Question Generation and Answering systems on the considered tasks, which are seldom available.To this purpose, we propose a method to build synthetic multimodal corpora enabling to train multimodal components for a data-QuestEval metric.The resulting metric is reference-less and multimodal; it obtains state-of-the-art correlations with human judgment on the WebNLG and WikiBio benchmarks.We make data-QUESTEVAL's code and models available for reproducibility purpose, as part of the QUESTEVAL project. 1
Empirical Studies on Software Refactoring Techniques in the Industrial Setting

Software refactoring is an approach that aims to improve software system quality by restructuring its internal design without altering the functionality of the software system. The software refactoring has gained comprehensive attention from the research community in software engineering since its emerging in 1999. Empirical studies were carried out to investigate whether the software refactoring indeed can achieve improvement in the software system quality. It is widely believed that refactoring has a favorable quality impact. However, several researchers have opposed this belief. This paper presents a systematic literature review (SLR) of the empirical studies that have been conducted in the industrial setting. Twenty studies were chosen and analyzed in depth as primary studies.The objectives of this SLR are to identify the refactoring techniques examined, and software quality attributes investigated as well as to analyze the connection between the refactoring techniques and the software quality attributes based on the selected empirical studies to understand the situation. The findings showed a lack of empirical research on the effect of the refactoring on software quality. Additionally, the relationship between the refactoring techniques and software quality attributes is unclear. Thus, several recommendations have been proposed to address these gaps.
MIDAS: A Dialog Act Annotation Scheme for Open Domain HumanMachine Spoken Conversations

Dialog act prediction in open-domain conversations is an essential language comprehension task for both dialog system building and discourse analysis.Previous dialog act schemes, such as SWBD-DAMSL, are designed mainly for discourse analysis in humanhuman conversations.In this paper, we present a dialog act annotation scheme, MIDAS (Machine Interaction Dialog Act Scheme), targeted at open-domain human-machine conversations.MIDAS is designed to assist machines to improve their ability to understand human partners.MIDAS has a hierarchical structure and supports multi-label annotations.We collected and annotated a large open-domain human-machine spoken conversation dataset (consisting of 24K utterances).To validate our scheme, we leveraged transfer learning methods to train a multi-label dialog act prediction model and reached an F1 score of 0.79. 1 1 Code, data, and trained models are available at https:
Exploring preservice teacher self-reported and enacted TPACK after participating in a learning activity types short course

Preparing preservice teachers to teach effectively with digital technologies and media (DTM) is at the heart of initial teacher preparation as digital access continues to increase throughout society. The development of preservice teacher technological pedagogical content knowledge (TPACK) is important in order to create DTM-rich instruction. This study investigated preservice teacher self-reported and enacted TPACK after participating in learning activity types short courses, and the relationship between preservice teacher perceptions and observed practice. Data collection occurred using a pre-post self-report survey and preservice teacher lesson plans collected before and after short course implementation in two educational technology courses. Results from structural equation modeling and confirmatory factor analysis indicated no significant differences in preservice teacher self-reported technological content knowledge (TCK), technological pedagogical knowledge (TPK), and TPACK, significant differences in preservice teacher enacted TCK, TPK, and TPACK, and no significant relationship between preservice teacher self-reported and enacted TPACK.
A Risk Analysis on Blockchain Technology Usage for Electronic Health Records

Health data is one of the most valuable data and highly sensitive. Its careful handling is essential in today' s digitalized world and cloud technology use for sharing. Health Information Systems facilitate the storage and accessibility of health data for better care along the patient path. As the integration of all historical patient data, the Electronic Health Record is at the heart of health data management. The centralization of stored health data represents a single point of failure and trust, making data exchange across institutions difficult and insecure. Blockchain technology builds on consensus mechanisms and immutable chains of blocks for validating and securing data transactions as a modern decentralized approach. The application of blockchain technology for Electronic Health Records is promising but still a young concept. Due to the wide range of discussion, this paper aims at identifying risks by using blockchain technology in the eHealth sector. Based on a systematic literature review, various authors' argumentations and findings are examined and concluded to set up the empirical study. The semistructured qualitative interview study aims to find out the threats of blockchain. The paper concludes with an overall discussion and some implementation recommendations.
Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training

Pre-trained multilingual language encoders, such as multilingual BERT and XLM-R, show great potential for zero-shot cross-lingual transfer.However, these multilingual encoders do not precisely align words and phrases across languages.Especially, learning alignments in the multilingual embedding space usually requires sentence-level or word-level parallel corpora, which are expensive to be obtained for low-resource languages.An alternative is to make the multilingual encoders more robust; when fine-tuning the encoder using downstream task, we train the encoder to tolerate noise in the contextual embedding spaces such that even if the representations of different languages are not aligned well, the model can still achieve good performance on zero-shot cross-lingual transfer.In this work, we propose a learning strategy for training robust models by drawing connections between adversarial examples and the failure cases of zero-shot cross-lingual transfer.We adopt two widely used robust training methods, adversarial training and randomized smoothing, to train the desired robust model.The experimental results demonstrate that robust training improves zero-shot cross-lingual transfer on text classification tasks.The improvement is more significant in the generalized crosslingual transfer setting, where the pair of input sentences belong to two different languages.
Paladin: an annotation tool based on active and proactive learning

In this paper, we present Paladin, an opensource web-based annotation tool for creating high-quality multi-label document-level datasets.By integrating active learning and proactive learning to the annotation task, Paladin makes the task less time-consuming and requiring less human effort.Although Paladin is designed for multi-label settings, the system is flexible and can be adapted to other tasks in single-label settings.
Interactive Image Inpainting of Large-Scale Missing Region

Image inpainting is a challenging reconstruction of the damaged image in photography, especially for more valued artwork than before.The damages are mostly caused by scratches and worn out, so they cannot be easily fixed physically.Thus, many scientists proposed sophisticated methods for restoring the damaged image into a new one similar to an original image.However, these methods have not solved the problem effectively if the missing region is large.In this paper, we focus on how to restore a large missing region in image inpainting.This algorithm is composed of two steps: structure propagation and color propagation.In structure propagation, we segment a large region (non-homogeneous) into several small regions (homogeneous) based on the salient structure of missing region.Then, we applied a simple pixel-based inpainting method called the Fast Marching Method (FMM) to fill in the missing homogeneous regions by color propagation.In the experimental section, we applied several kinds of missing regions, such as irregular and regular missing regions, in large sizes.The results show that our proposed method performs well in various conditions.
How to enhance quantum generative adversarial learning of noisy information

Abstract Quantum machine learning is where nowadays machine learning (ML) meets quantum information science. In order to implement this new paradigm for novel quantum technologies, we still need a much deeper understanding of its underlying mechanisms, before proposing new algorithms to feasibly address real problems. In this context, quantum generative adversarial learning is a promising strategy to use quantum devices for quantum estimation or generative ML tasks. However, the convergence behaviours of its training process, which is crucial for its practical implementation on quantum processors, have not been investigated in detail yet. Indeed here we show how different training problems may occur during the optimization process, such as the emergence of limit cycles. The latter may remarkably extend the convergence time in the scenario of mixed quantum states playing a crucial role in the already available noisy intermediate scale quantum devices. Then, we propose new strategies to achieve a faster convergence in any operating regime. Our results pave the way for new experimental demonstrations of such hybrid classical-quantum protocols allowing to evaluate the potential advantages over their classical counterparts.
Advanced Binary Matrix-Based Frequent Pattern Mining Algorithm

Frequent pattern mining (FPM) is one of the most important areas in the field of data mining. Several FPM algorithms have been proposed in the literature by many researchers. In most of the approaches, data set is scanned repeatedly in almost every steps of the algorithm that leads to high time complexity. That is why, processing huge amount of data using those algorithms may not be a suitable option. Hence, a novel FPM algorithm is proposed in this paper that improves efficiency by decreasing the time complexity as compared to classical frequent pattern mining algorithm. The proposed FPM algorithm converts the real-world data set into a binary matrix in a single scan, then join operation is performed to obtain the candidate itemsets. Further, AND operation is performed on the candidates to obtain frequent itemsets. Further more, using our proposed algorithm, interesting association rules can be derived.
GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition

Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules.However, devising labeling rules is challenging because it often requires a considerable amount of manual effort and domain expertise.To alleviate this problem, we propose GLARA, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data.We first create a graph with nodes representing candidate rules extracted from unlabeled data.Then, we design a new graph neural network to augment labeling rules by exploring the semantic relations between rules.We finally apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data.We evaluate our method on three NER datasets and find that we can achieve an average improvement of +20% F1 score over the best baseline when given a small set of seed rules.
Penerapan Sistem Informasi Administrasi Perpustakaan Menggunakan Model Desain User Experience

The library at SMP N 5 Bandarlampung is a junior high school library that has library management staff and the number of book collections according to library standards and has adequate library facilities and infrastructure. Students and students are required to become members of the school library to support the learning process. However, the process of borrowing and returning books currently carried out is still conventional, that is, all library administration data collection in borrowing and returning books is still written in the book, and when looking for the required data, you must open the book page. Application of library administration information systems using this user experience design, users can better understand and easily use applications that are made to maximize the performance of library staff, school principals, and students in managing library administration. The design of the library administration information system using the user experience design at SMP Negeri 5 Bandarlampung using the user centered design method has 5 stages namely, Empethized, Define Problem Statements, Indentation, and Prototype in accordance with the needs of users in the library administration information system based on the user experience design undertaken. The results of testing the criteria for the DeLone and McLean Model criteria for the success of the information system were 84.31% with the criteria being Very Good .
Emotion-Regularized Conditional Variational Autoencoder for Emotional Response Generation

This article presents an emotion-regularized conditional variational autoencoder (Emo-CVAE) model for generating emotional conversation responses. In conventional CVAE-based emotional response generation, emotion labels are simply used as additional conditions in prior, posterior and decoder networks. Considering that emotion styles are naturally entangled with semantic contents in the language space, the Emo-CVAE model utilizes emotion labels to regularize the CVAE latent space by introducing an extra emotion prediction network. In the training stage, the estimated latent variables are required to predict the emotion labels and token sequences of the input responses simultaneously. Experimental results show that our Emo-CVAE model can learn a more informative and structured latent space than a conventional CVAE model and output responses with better content and emotion performance than baseline CVAE and sequence-to-sequence (Seq2Seq) models.
The Effect of Moodle E-Learning Material on EFL Reading Comprehension

Reading comprehension depicted as the most important skills and a complex cognitive process for the achievement of success or learning outcome in both school and society where reader’s prior knowledge and experiences serve central roles in the interpretation of a text or the interaction of the reader, the text, and the context. The purpose of the exploratory study was to investigate the effect of the Moodle e-learning on EFL reading comprehension. 27 EFL college students were participating in the study. Data were collected through the pretest and posttest of reading comprehension. Data analysis involved three stages: normality test, homogeneity test, and hypothesis testing by utilizing software statistical package for social science (SPSS) for the window. Results indicate that the use of Moodle e-learning has a significant effect on improving students’ reading comprehension.
Unsupervised Abstractive Summarization of Bengali Text Documents

Abstractive summarization systems generally rely on large collections of documentsummary pairs.However, the performance of abstractive systems remains a challenge due to the unavailability of the parallel data for low-resource languages like Bengali.To overcome this problem, we propose a graph-based unsupervised abstractive summarization system in the single-document setting for Bengali text documents, which requires only a Part-Of-Speech (POS) tagger and a pre-trained language model trained on Bengali texts.We also provide a human-annotated dataset with document-summary pairs to evaluate our abstractive model and to support the comparison of future abstractive summarization systems of the Bengali Language.We conduct experiments on this dataset and compare our system with several well-established unsupervised extractive summarization systems.Our unsupervised abstractive summarization model outperforms the baselines without being exposed to any human-annotated reference summaries.
Characterizing Interaction Design by Its Ideals: A Discipline in Transition

As a way to capture a broadly acceptable high-level characterization of design, we focus on the guiding values or ideals of the discipline. We first reason from the notion of engineering interfaces for usability and utility up to the 1990s to the current ideal of designing interfaces for experience and meaning. Next, we identify three recent technical and societal developments that are challenging the existing ideals of interaction design, namely the move towards hybrid physical/digital materials, the emergence of an increasingly complex and fluid digital ecology, and the increasing proportion of autonomous or partially autonomous systems changing their behavior over time and with use. These challenges in turn motivate us to propose three directions in which new ideals for interaction design might be sought: the first is to go beyond the language-body divide that implicitly frames most of our current understandings of experience and meaning, the second is to extend the scope of interaction design from individual interfaces to the complex sociotechnical fabric of human and nonhuman actors, and the third is to go beyond predictability by learning to design with machine learning.
Analyzing Continuance of Cloud Computing in Higher Education Institutions: Should We Stay, or Should We Go?

Higher Education Institutions (HEIs) consider resource optimization as an essential concern. Cloud computing (CC) in the fourth industrial revolution became the de-facto standard for delivering IT resources and services. CC is now a mainstream technology, andHEIs across the globe are rapidly transitioning to this model; hence, maintaining the retention of the customers of such technologies is challenging for cloud service providers. Current research concerning CC focused on adoption and acceptance. However, there is still a scarcity of research concerning such technology’s continued use in an organizational setting. Drawing on the prior literature in organizational-level continuance, this paper established a positivist quantitative-empirical study to bridge the research gap and assess the precursors for a continuance of cloud technology in HEIs. Subsequently, this study developed a conceptual framework by integrating the IS success model and the IS discontinuance model through the lens of the TOE framework. The data were collected from the decision-makers of Malaysian HEIs that have adopted CC services, and analyzed using Structural equation Modelling (SEM) based on Partial Least Squares (PLS). The results indicate that the continuance intention can be predicted by technology, organizational, environmental, and other contextualized factors, explaining 85.2% of the dependent variables’ variance. The paper closes with a discussion of the research limitations, contribution, and future directions.
Alternating Recurrent Dialog Model with Large-scale Pre-trained Language Models

Existing dialog system models require extensive human annotations and are difficult to generalize to different tasks.The recent success of large pre-trained language models has suggested the effectiveness of incorporating language priors in down-stream NLP tasks.However, how much pre-trained language models can help dialog response generation is still under exploration.In this paper, we propose a simple, general, and effective framework: Alternating Recurrent Dialog Model (ARDM) 1 .ARDM models each speaker separately and takes advantage of large pre-trained language models.It requires no supervision from human annotations such as belief states or dialog acts to achieve effective conversations.ARDM outperforms or is on par with the state-of-theart methods on two popular task-oriented dialog datasets: CamRest676 and MultiWOZ.Moreover, we can generalize ARDM to more challenging, non-collaborative tasks such as persuasion.In the PersuasionForGood task, ARDM is capable of generating human-like responses to persuade people to donate to a charity.
Automated Quantum Hardware Selection for Quantum Workflows

The execution of a quantum algorithm typically requires various classical pre- and post-processing tasks. Hence, workflows are a promising means to orchestrate these tasks, benefiting from their reliability, robustness, and features, such as transactional processing. However, the implementations of the tasks may be very heterogeneous and they depend on the quantum hardware used to execute the quantum circuits of the algorithm. Additionally, today’s quantum computers are still restricted, which limits the size of the quantum circuits that can be executed. As the circuit size often depends on the input data of the algorithm, the selection of quantum hardware to execute a quantum circuit must be done at workflow runtime. However, modeling all possible alternative tasks would clutter the workflow model and require its adaptation whenever a new quantum computer or software tool is released. To overcome this problem, we introduce an approach to automatically select suitable quantum hardware for the execution of quantum circuits in workflows. Furthermore, it enables the dynamic adaptation of the workflows, depending on the selection at runtime based on reusable workflow fragments. We validate our approach with a prototypical implementation and a case study demonstrating the hardware selection for Simon’s algorithm.
Extremely Small BERT Models from Mixed-Vocabulary Training

Pretrained language models like BERT have achieved good results on NLP tasks, but are impractical on resource-limited devices due to memory footprint.A large fraction of this footprint comes from the input embeddings with large input vocabulary and embedding dimensions.Existing knowledge distillation methods used for model compression cannot be directly applied to train student models with reduced vocabulary sizes.To this end, we propose a distillation method to align the teacher and student embeddings via mixed-vocabulary training.Our method compresses BERT LARGE to a task-agnostic model with smaller vocabulary and hidden dimensions, which is an order of magnitude smaller than other distilled BERT models and offers a better size-accuracy trade-off on language understanding benchmarks as well as a practical dialogue task.