Reviewerly/exHarmony huggingface dataset:
A Compare Aggregate Transformer for Understanding Document-grounded Dialogue

Unstructured documents serving as external knowledge of the dialogues help to generate more informative responses.Previous research focused on knowledge selection (KS) in the document with dialogue.However, dialogue history that is not related to the current dialogue may introduce noise in the KS processing.In this paper, we propose a Compare Aggregate Transformer (CAT) to jointly denoise the dialogue context and aggregate the document information for response generation.We designed two different comparison mechanisms to reduce noise (before and during decoding).In addition, we propose two metrics for evaluating document utilization efficiency based on word overlap.Experimental results on the CMU DoG dataset show that the proposed CAT model outperforms the state-ofthe-art approach and strong baselines.
Enhancing Static Charts With Data-Driven Animations

Static visual attributes such as color and shape are used with great success in visual charts designed to be displayed in static, hard-copy form. However, nowadays digital displays become ubiquitous in the visualization of any form of data, lifting the confines of static presentations. In this article, we propose incorporating data-driven animations to bring static charts to life, with the purpose of encoding and emphasizing certain attributes of the data. We lay out a design space for data-driven animated effects and experiment with three versatile effects, marching ants, geometry deformation and gradual appearance. For each, we provide practical details regarding their mode of operation and extent of interaction with existing visual encodings. We examine the impact and effectiveness of our enhancements through an empirical user study to assess preference as well as gauge the influence of animated effects on human perception in terms of speed and accuracy of visual understanding.
IsOBS: An Information System for Oracle Bone Script

Oracle bone script (OBS) is the earliest known ancient Chinese writing system and the ancestor of modern Chinese.As the Chinese writing system is the oldest continuously-used system in the world, the study of OBS plays an important role in both linguistic and historical research.In order to utilize advanced machine learning methods to automatically process OBS, we construct an information system for OBS (IsOBS) to symbolize, serialize, and store OBS data at the character-level, based on efficient databases and retrieval modules.Moreover, we also apply few-shot learning methods to build an effective OBS character recognition module, which can recognize a large number of OBS characters (especially those characters with a handful of examples) and make the system easy to use.The demo system of IsOBS can be found from http: //isobs.thunlp.org/.In the future, we will add more OBS data to the system, and hopefully our IsOBS can support further efforts in automatically processing OBS and advance the scientific progress in this field.
DRS at MRP 2020: Dressing up Discourse Representation Structures as Graphs

Discourse Representation Theory (DRT) is a formal account for representing the meaning of natural language discourse.Meaning in DRT is modeled via a Discourse Representation Structure (DRS), a meaning representation with a model-theoretic interpretation, which is usually depicted as nested boxes.In contrast, a directed labeled graph is a common data structure used to encode semantics of natural language texts.The paper describes the procedure of dressing up DRSs as directed labeled graphs to include DRT as a new framework in the 2020 shared task on Cross-Framework and Cross-Lingual Meaning Representation Parsing.Since one of the goals of the shared task is to encourage unified models for several semantic graph frameworks, the conversion procedure was biased towards making the DRT graph framework somewhat similar to other graph-based meaning representation frameworks.* Part of the work was done while the author was at the University of Groningen.1 Throughout the paper, we mean a directed labeled graph when simply talking about graphs, unless stated otherwise.abstraction and serialization for cross-framework meaning representation parsing.
Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation

Crowdsourced data used in machine learning services might carry sensitive information about attributes that users do not want to share. Various methods have been proposed to minimize the potential information leakage of sensitive attributes while maximizing the task accuracy. However, little is known about the theory behind these methods. In light of this gap, we develop a novel theoretical framework for attribute obfuscation. Under our framework, we propose a minimax optimization formulation to protect the given attribute and analyze its inference guarantees against worst-case adversaries. Meanwhile, it is clear that in general there is a tension between minimizing information leakage and maximizing task accuracy. To understand this, we prove an information-theoretic lower bound to precisely characterize the fundamental trade-off between accuracy and information leakage. We conduct experiments on two real-world datasets to corroborate the inference guarantees and validate this trade-off. Our results indicate that, among several alternatives, the adversarial learning approach achieves the best trade-off in terms of attribute obfuscation and accuracy maximization.
I miss you babe: Analyzing Emotion Dynamics During COVID-19 Pandemic

With the world on a lockdown due to the COVID-19 pandemic, this paper studies emotions expressed on Twitter.Using a combined strategy of time series analysis of emotions augmented by tweet topics, this study provides an insight into emotion transitions during the pandemic.After tweets are annotated with dominant emotions and topics, a timeseries emotion analysis is used to identify disgust and anger as the most commonly identified emotions.Through longitudinal analysis of each user, we construct emotion transition graphs, observing key transitions between disgust and anger, and self-transitions within anger and disgust emotional states.Observing user patterns through clustering of user longitudinal analyses reveals emotional transitions fall into four main clusters: (1) erratic motion over short period of time, (2) disgust → anger, (3) optimism → joy.(4) erratic motion over a prolonged period.Finally, we propose a method for predicting users subsequent topic, and by consequence their emotions, through constructing an Emotion Topic Hidden Markov Model, augmenting emotion transition states with topic information.Results suggests that the predictions fare better than baselines, spurring directions of predicting emotional states based on Twitter posts.
Korean Historical Documents Analysis with Improved Dynamic Word Embedding

Historical documents refer to records or books that provide textual information about the thoughts and consciousness of past civilisations, and therefore, they have historical significance. These documents are used as key sources for historical studies as they provide information over several historical periods. Many studies have analysed various historical documents using deep learning; however, studies that employ changes in information over time are lacking. In this study, we propose a deep-learning approach using improved dynamic word embedding to determine the characteristics of 27 kings mentioned in the Annals of the Joseon Dynasty, which contains a record of 500 years. The characteristics of words for each king were quantitated based on dynamic word embedding; further, this information was applied to named entity recognition and neural machine translation.In experiments, we confirmed that the method we proposed showed better performance than other methods. In the named entity recognition task, the F1-score was 0.68; in the neural machine translation task, the BLEU4 score was 0.34. We demonstrated that this approach can be used to extract information about diplomatic relationships with neighbouring countries and the economic conditions of the Joseon Dynasty.
Fine-Tuning MT systems for Robustness to Second-Language Speaker Variations

The performance of neural machine translation (NMT) systems only trained on a single language variant degrades when confronted with even slightly different language variations.With this work, we build upon previous work to explore how to mitigate this issue.We show that fine-tuning using naturally occurring noise along with pseudo-references (i.e."corrected" non-native inputs translated using the baseline NMT system) is a promising solution towards systems robust to such type of input variations.We focus on four translation pairs, from English to Spanish, Italian, French, and Portuguese, with our system achieving improvements of up to 3.1 BLEU points compared to the baselines, establishing a new stateof-the-art on the JFLEG-ES dataset. 1
A Meta-Analysis of Industrial Security Research for Sustainable Organizational Growth

As the world enters a fourth Industrial Revolution, organizations worldwide face challenges in dealing with important assets such as industrial technology. Leaking these assets can not only damage organizations economically but also negatively affect customer relationships and brand image. This has led to an increased awareness of industrial security in both the business and academic spheres and a focus on identifying and implementing countermeasures against security incidents, as future computing environments will continuously face security threats. This study first examines the literature on industrial security and its current status in South Korea, which is considered an active industrial security research environment. Subsequently, meta-analyses are conducted on South Korea and abroad to compare their status and research trends in the industrial security field. The results confirm that South Korea has more actively conducted relevant studies compared to international research. This study contributes to the current literature by not only increasing the awareness of industrial security but also encouraging future studies in the field to facilitate a safe and sustainable computing environment.
Cross-View Image Retrieval - Ground to Aerial Image Retrieval Through Deep Learning

Cross-modal retrieval aims to measure the content similarity between different types of data. The idea has been previously applied to visual, text, and speech data. In this paper, we present a novel cross-modal retrieval method specifically for multi-view images, called Cross-view Image Retrieval CVIR. Our approach aims to find a feature space as well as an embedding space in which samples from street-view images are compared directly to satellite-view images (and vice-versa). For this comparison, a novel deep metric learning based solution "DeepCVIR" has been proposed. Previous cross-view image datasets are deficient in that they (1) lack class information; (2) were originally collected for cross-view image geolocalization task with coupled images; (3) do not include any images from off-street locations. To train, compare, and evaluate the performance of cross-view image retrieval, we present a new 6 class cross-view image dataset termed as CrossViewRet which comprises of images including freeway, mountain, palace, river, ship, and stadium with 700 high-resolution dual-view images for each class. Results show that the proposed DeepCVIR outperforms conventional matching approaches on CVIR task for the given dataset and would also serve as the baseline for future research.
A Survey on Image Retrieval Techniques

In the recent years, with the rapid development of technologies production, digital images tend to grow in exponential order. This makes the challenge in retrieving image. Image retrieval plays a vital role in various fields such as Medical, Tourism promotion, Forensics Lab etc. In order to handle these rapid growth efficient images retrieval methods need to be developed. This paper is presented with the survey of different Image retrieval techniques which used various techniques from visual features to the latest deep learning with Convolutional Neural Network(CNN) which become the best approach for image retrieval with number of layers applicable for large database. This survey concretes the way to understand the processing methods of Content Based Image Retrieval (CBIR).
EFL LEARNERS’ CODE-SWITCHING: Why do they switch the language?

This qualitative study aims to figure out the factors that influence EFL learners’ code-switching. In collecting data, a semi-structured interview was conducted in which involved five EFL learners as the participants. Those participants were selected through the purposive sampling method: being second-semester students of the English Language Education Department of Universitas Islam Negeri Ar-Raniry, and taking Basic Speaking class. Semi-structured interviewed was applied to collect the data needed. The finding of this study showed that all participants employed code-switching in their class. The reasons for EFL learners’ code-switching were influenced by participants’ roles and relationships, selected topics, and interactions. Thus, code-switching was regarded as a useful strategy that helped the learners convey their ideas to be fully understood and meaningful in the interaction process.
The impact of differences in text segmentation on the automated quantitative evaluation of song-lyrics

The text-evaluation application Coh-Metrix and natural language processing rely on the sentence for text segmentation and analysis and frequently detect sentence limits by means of punctuation. Problems arise when target texts such as pop song lyrics do not follow formal standards of written text composition and lack punctuation in the original. In such cases it is common for human transcribers to prepare texts for analysis, often following unspecified or at least unreported rules of text normalization and relying potentially on an assumed shared understanding of the sentence as a text-structural unit. This study investigated whether the use of different transcribers to insert typographical symbols into song lyrics during the pre-processing of textual data can result in significant differences in sentence delineation. Results indicate that different transcribers (following commonly agreed-upon rules of punctuation based on their extensive experience with language and writing as language professionals) can produce differences in sentence segmentation. This has implications for the analysis results for at least some Coh-Metrix measures and highlights the problem of transcription, with potential consequences for quantification at and above sentence level. It is argued that when analyzing non-traditional written texts or transcripts of spoken language it is not possible to assume uniform text interpretation and segmentation during pre-processing. It is advisable to provide clear rules for text normalization at the pre-processing stage, and to make these explicit in documentation and publication.
Improving Word Embedding Factorization for Compression Using Distilled Nonlinear Neural Decomposition

Word-embeddings are vital components of Natural Language Processing (NLP) models and have been extensively explored.However, they consume a lot of memory which poses a challenge for edge deployment.Embedding matrices, typically, contain most of the parameters for language models and about a third for machine translation systems.In this paper, we propose Distilled Embedding, an (input/output) embedding compression method based on low-rank matrix decomposition and knowledge distillation.First, we initialize the weights of our decomposed matrices by learning to reconstruct the full pre-trained wordembedding and then fine-tune end-to-end, employing knowledge distillation on the factorized embedding.We conduct extensive experiments with various compression rates on machine translation and language modeling, using different data-sets with a shared wordembedding matrix for both embedding and vocabulary projection matrices.We show that the proposed technique is simple to replicate, with one fixed parameter controlling compression size, has higher BLEU score on translation and lower perplexity on language modeling compared to complex, difficult to tune state-of-theart methods.
Characterizing the Impact of Using Features Extracted from Pre-trained Models on the Quality of Video Captioning Sequence-to-Sequence Models

The task of video captioning, that is, the automatic generation of sentences describing a sequence of actions in a video, has attracted an increasing attention recently. The complex and high-dimensional representation of video data make it difficult for a typical encoder-decoder architectures to recognize relevant features and encode them in a proper format. Video data contains different modalities that can be recognized using a combination of image, scene, action and audio features. In this paper, we characterize the different features affecting video descriptions and explore the interactions among these features and how they affect the final quality of a video representation. Built on existing encoder-decoder models that utilize limited range of video information, our comparisons show how the inclusion of multi-modal video features can make a significant effect on improving the quality of generated statements. The work is of special interest to scientists and practitioners who are using sequence-to-sequence models to generate video captions.
Development of a Fast Algorithm of Number- Theoretic Signal Transformation for OFDM Communication Systems Using UFMC Technology

Modern OFDM communication systems must have the property of adapting to the changing parameters of the communication channel.Intelligent decision support systems using UFMS technology can effectively solve this problem.The use of UFMS filtering allows you to select the optimal value of the cyclic prefix and increase the spectral efficiency of the OFDM signal.However, this increases the computational cost of performing digital filtering procedures, which leads to an increase in network latency.Therefore, the development of a fast algorithm for performing numerical-theoretical signal transformations for OFDM systems using UFMC technology is an urgent task.
Rethinking Topic Modelling: From Document-Space to Term-Space

This paper problematizes the reliance on documents as the basic notion for defining term interactions in standard topic models.As an alternative to this practice, we reformulate topic distributions as latent factors in term similarity space.We exemplify the idea using a number of standard word embeddings built with very wide context windows.The embedding spaces are transformed to sparse similarity spaces, and topics are extracted in standard fashion by factorizing to a lower-dimensional space.We use a number of different factorization techniques, and evaluate the various models using a large set of evaluation metrics, including previously published coherence measures, as well as a number of novel measures that we suggest better correspond to real-world applications of topic models.Our results clearly demonstrate that term-based models outperform standard document-based models by a large margin.
A Data-Centric Framework for Composable NLP Workflows

Empirical natural language processing (NLP) systems in application domains (e.g., healthcare, finance, education) involve interoperation among multiple components, ranging from data ingestion, human annotation, to text retrieval, analysis, generation, and visualization.We establish a unified open-source framework to support fast development of such sophisticated NLP workflows in a composable manner.The framework introduces a uniform data representation to encode heterogeneous results by a wide range of NLP tasks.It offers a large repository of processors for NLP tasks, visualization, and annotation, which can be easily assembled with full interoperability under the unified representation.The highly extensible framework allows plugging in custom processors from external off-the-shelf NLP and deep learning libraries.The whole framework is delivered through two modularized yet integratable open-source projects, namely Forte 1 (for workflow infrastructure and NLP function processors) and Stave 2 (for user interaction, visualization, and annotation).
Analysis of in-Service and Pre-Service Social Studies Teachers' Digital Citizenship

Digital citizenship is a concept revealed by the rapid development of information and communication technologies.Digital citizenship is to be able to join the community online.This study was conducted to reveal the digital citizenship status of social studies teachers and pre-service social studies teachers.The study was conducted in the 2018-2019 academic year and an exploratory mixed research method was used.The study group consisted of 1041 people, including 512 social studies teachers and 529 pre-service social studies teachers in the quantitative dimension, 16 social studies teachers and 8 pre-service social studies teachers were interviewed in the direction of the purposeful sampling in the qualitative dimension.As a data collection tool, the "Digital Citizenship Scale" which was developed by Glassman, Choi and Cristol (2017) and adapted to Turkish by Kara (2018) was used in the quantitative design.On the other hand, data were collected by interview technique in the qualitative design.Quantitative data were transferred to SPSS program and descriptive analysis, independent t-test, correlation analysis and standard multiple regression analysis were used in the statistical analysis of the data.Qualitative data were collected using the interview technique and the data were analyzed with content analysis.As a result of the study, when the digital citizenship status of the participants was examined in line with the qualitative and quantitative data, it was observed that they found themselves quite sufficient in terms of digital skills, and they thought that online platforms increased their local/global awareness positively.The participants were also observed to tend to be hesitant in the critical thinking dimension and to be hesitant in cooperative internet activities and political activities.Digital citizenship status by gender was statistically significant in the dimensions of local/global awareness, critical thinking, cooperative internet activities and political activities in terms of gender and this was in favor of males -supported by quallitative data -.Also, it was observed that the dimensions of digital citizenship did not show a statistically significant difference according to the status of starting the profession.Qualitative data showed that pre-service teachers are more active in online environments than teachers.When looked at the connection between daily internet usage of teachers and pre-service teachers and digital citizenship dimensions, a negative correlation was found between them.