Reviewerly/exHarmony huggingface dataset:
A DISCOURSE-BASED INFORMATION RETRIEVAL FOR TAMIL LITERARY TEXTS

Tamil literature has many valuable thoughts that can help the human community to lead a successful and a happy life. Tamil literary works are abundantly available and searched on the World Wide Web (WWW), but the existing search systems follow a keyword-based match strategy which fails to satisfy the user needs. This necessitates the demand for a focused Information Retrieval System that semantically analyses the Tamil literary text which will eventually improve the search system performance. This paper proposes a novel Information Retrieval framework that uses discourse processing techniques which aids in semantic analysis and representation of the Tamil Literary text. The proposed framework has been tested using two ancient literary works, the Thirukkural and Naladiyar, which were written during 300 BCE. The Thirukkural comprises 1330 couplets, each 7 words long, while the Naladiyar consists of 400 quatrains, each 15 words long. The proposed system, tested with all the 1330 Thirukkural couplets and 400 Naladiyar quatrains, achieved a mean average precision (MAP) score of 89%. The performance of the proposed framework has been compared with Google Tamil search and a keyword-based search which is a substandard version of the proposed framework. Google Tamil search achieved a MAP score of 56% and keyword-based method achieved a MAP score of 62% which shows that the discourse processing techniques improves the search performance of an Information Retrieval system.
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models?

Recently, mT5 -a massively multilingual version of T5 -leveraged a unified text-to-text format to attain state-of-the-art results on a wide variety of multilingual NLP tasks.In this paper, we investigate the impact of incorporating parallel data into mT5 pre-training.We find that multi-tasking language modeling with objectives such as machine translation during pretraining is a straightforward way to improve performance on downstream multilingual and cross-lingual tasks.However, the gains start to diminish as the model capacity increases, suggesting that parallel data might not be as essential for larger models.At the same time, even at larger model sizes, we find that pre-training with parallel data still provides benefits in the limited labelled data regime.
Bayesian Differential Privacy for Linear Dynamical Systems

Differential privacy is a privacy measure based on the difficulty of discriminating between similar input data. In differential privacy analysis, similar data usually implies that their distance does not exceed a predetermined threshold. It, consequently, does not take into account the difficulty of distinguishing data sets that are far apart, which often contain highly private information. This problem has been pointed out in the research on differential privacy for static data, and Bayesian differential privacy has been proposed, which provides a privacy protection level even for outlier data by utilizing the prior distribution of the data. In this letter, we introduce this Bayesian differential privacy to dynamical systems, and provide privacy guarantees for distant input data pairs and reveal its fundamental property. For example, we design a mechanism that satisfies the desired level of privacy protection, which characterizes the trade-off between privacy and information utility.
Collaborative Sense-Making in Genomic Research: The Role of Visualisation

Genomic research emerges from collaborative work within and across different scientific disciplines. A diverse range of visualisation techniques has been employed to aid this research, yet relatively little is known as to how these techniques facilitate collaboration. We conducted a case study of collaborative research within a biomedical institute to learn more about the role visualisation plays in genomic mapping. Interviews were conducted with molecular biologists (N = 5) and bioinformaticians (N = 6). We found that genomic research comprises a variety of distinct disciplines engaged in complex analytic tasks that each resist simplification, and their complexity influences how visualisations were used. Visualisation use was impacted by group-specific interactions and temporal work patterns. Visualisations were also crucial to the scientific workflow, used for both question formation and confirmation of hypotheses, and acted as an anchor for the communication of ideas and discussion. In the latter case, two approaches were taken: providing collaborators with either interactive or static imagery representing a viewpoint. The use of generic software for simplified visualisations, and quick production and curation was also noted. We discuss these findings with reference to group-specific interactions and present recommendations for improving collaborative practices through visual analytics.
Learning Robust Latent Representations for Controllable Speech Synthesis

State-of-the-art Variational Auto-Encoders (VAEs) for learning disentangled latent representations give impressive results in discovering features like pitch, pause duration, and accent in speech data, leading to highly controllable text-to-speech (TTS) synthesis.However, these LSTM-based VAEs fail to learn latent clusters of speaker attributes when trained on limited or noisy datasets.Further, different latent variables are found to encode the same features, limiting the control and expressiveness during speech synthesis.To resolve these issues, we propose REMMI (Reordered transformer Encoder with Minimal Mutual Information) where we minimize the mutual information between different latent variables and devise a modified Transformer architecture with layer reordering to learn controllable latent representations in speech data.We show that REMMI reduces the cluster overlap of speaker attributes by at least 30% over LSTM-VAE.
COVID-19 Tracing Apps as a Legal Problem: An Investigation of the Norwegian ‘Smittestopp’ App

In their efforts to curb the COVID-19 pandemic, many countries have introduced contact tracing apps installed on mobile phones with the aim of breaking chains of infection.This raises ethical and legal questions, as these apps have the potential to be used for surveillance of the population.There is pressure to set privacy and data protection aside to allow extensive collection and processing of personal data, while the benefits of the apps remain uncertain.The two versions of the Norwegian COVID-19 tracing app are used as a case study to explore how law and legal norms are made and implemented-or not-in the context of a public emergency.In Norway, the legal question of contact tracing apps has largely been limited to a question of compliance with the GDPR and has excluded a meaningful conversation about the use of apps as pandemic response tools and their impact on rights and freedoms.The normative argument of the article is that to combine a robust form of privacy and data protection with the use of digital tools in a crisis, we need to scrutinize carefully the effects technology choices have on human rights and the rule of law.
Neural Sentence Simplification with Semantic Dependency Information

Most previous works on neural sentence simplification exploit seq2seq model to rewrite a sentence without explicitly considering the semantic information of the sentence. This may lead to the semantic deviation of the simplified sentence. In this paper, we leverage semantic dependency graph to aid neural sentence simplification system. We propose a new sentence simplification model with semantic dependency information, called SDISS (as shorthand for Semantic Dependency Information guided Sentence Simplification), which incorporates semantic dependency graph to guide sentence simplification. We evaluate SDISS on three benchmark datasets and it outperforms a number of strong baseline models on the SARI and FKGL metrics. Human evaluation also shows SDISS can produce simplified sentences with better quality.
The Style-Content Duality of Attractiveness: Learning to Write Eye-Catching Headlines via Disentanglement

Eye-catching headlines function as the first device to trigger more clicks, bringing reciprocal effect between producers and viewers. Producers can obtain more traffic and profits, and readers can have access to outstanding articles. When generating attractive headlines, it is important to not only capture the attractive content but also follow an eye-catching writtenstyle. In this paper, we propose a Disentanglement-based Attractive Headline Generator (DAHG) that generates headline which captures the attractive content following the attractive style. Concretely, we first devise a disentanglement module to divide the style and content of an attractive prototype headline into latent spaces, with two auxiliary constraints to ensure the two spaces are indeed disentangled. The latent content information is then used to further polish the document representation and help capture the salient part. Finally, the generator takes the polished document as input to generate headline under the guidance of the attractive style. Extensive experiments on the public Kuaibao dataset show that DAHG achieves state-of-the-art performance. Human evaluation also demonstrates that DAHG triggers 22% more clicks than existing models.
Inducing Semantic Roles Without Syntax

Semantic roles are a key component of linguistic predicate-argument structure, but developing ontologies of these roles requires significant expertise and manual effort.Methods exist for automatically inducing semantic roles using syntactic representations, but syntax can also be difficult to define, annotate, and predict.We show it is possible to automatically induce semantic roles from QA-SRL, a scalable and ontology-free semantic annotation scheme that uses question-answer pairs to represent predicate-argument structure.By associating arguments with distributions over QA-SRL questions and clustering them in a mixture model, our method outperforms all previous models as well as a new state-of-the-art baseline over gold syntax.We show that our method works because QA-SRL acts as surrogate syntax, capturing non-overt arguments and syntactic alternations, which are central motivators for the use of semantic role labeling systems. 1
FEATURES OF USING PROJECT-BASED TECHNOLOGIES IN THE PROCESS OF DISTANCE FOREIGN LANGUAGE LEARNING AT HIGHER EDUCATION INSTITUTIONS

The purpose of the article is to analyze the use of project-based technologies in the process of distance learning of a foreign language at higher education institutions. Based on the study of pedagogical, psychological and methodological literature, programmes and methodological documentations of higher education institutions and practical experience it was found that the use of project-based technologies in the process of distance learning of a foreign language at higher education institutions has been insufficiently studied. It has been noted that the technology of project-based learning allows the creation of pedagogical conditions for further development of a student’s creative abilities and personal traits, essential for successful professional activity in the future, which determines the relevance of the study. Theoretical (analysis of scientific sources, pedagogical, psychological literature, educational and methodical materials, comparison) and empirical (questionnaire, interviews, testing, generalization of pedagogical experience, observation) research methods were used to solve the tasks in order to understand the problem and identify difficulties associated with the use of innovative technologies, namely, project-based technologies, in the process of distance learning of a foreign language. The purpose of the article is to highlight the theoretical foundations and features of the use of project-based technologies in the process of distance learning of a foreign language at higher education institutions. It has been found that there is a problem associated with lack of professionalism in the use of innovative technologies in distance learning by teachers, which is negatively affects the efficiency, productivity and level of teaching, as well as lack of ability on the part of students to communicate in the information environment. It is substantiated that the project-based technology allows to teach students to acquire knowledge independently, to activate them, to be able to use the obtained information for solving of new cognitive and practical problems.
A Systematic Review of Mobile-Based Assessment Acceptance Studies from 2009 To 2019

Despite many studies being conducted on mobile learning acceptance, few investigate mobile-based assessment acceptance. The objectives of this research are to provide valuable insights into current research on mobile-based assessment literature, and to identify the main gaps in the mobile-based assessment acceptance literature. Therefore, the present study systematically reviews 48 previous studies and eight articles related to mobile-based assessment acceptance to provide a comprehensive analysis of the articles published from 2009 to 2019. Findings indicate that majority of mobile-based assessment studies focused on evaluating the effectiveness and performance of mobile-based assessment system and conducted at the secondary school level. In addition, this study identified several gaps. Further research is needed to study the acceptance problem of a mobile-based assessment system. More investigation is required to predict which external factors that can enhance the acceptance and use of mobile-based assessment among students. The findings of this review study provide a valuable reference for researchers about the current trend of mobile-based assessment research as well as the research gaps that should be covered in future studies.
Angström-Prescott empirical model to estimate solar radiation in Norte de Santander, Colombia

The document shows the application of the empirical Angström-Prescott model in different places in Norte de Santander, Colombia. The model estimates solar radiation from hours of sunlight, at a site where brightness and solar radiation are measured. The data were obtained from the Institute of Hydrology, Meteorology and Environmental Studies, IDEAM; algorithms were developed in RStudio to process and analyze the information. The model establishes a linear relationship between solar radiation and the hours of sunlight, in a specific geographic location. Therefore, regression analyzes were performed for three different sites, using historical records of brightness and solar radiation, obtaining the R-squared coefficients of: 0.73, 0.78, and 0.42. The models were then extrapolated to nearby regions with solar brightness records, but without solar radiation data, to obtain an estimate of radiation at these locations. Finally, a database was created with monthly average information on solar radiation for various subregions of Norte de Santander, which can be used for the design and implementation of photovoltaic systems.
Dialog Policy Learning for Joint Clarification and Active Learning Queries

Intelligent systems need to be able to recover from mistakes, resolve uncertainty, and adapt to novel concepts not seen during training. Dialog interaction can enable this by the use of clarifications for correction and resolving uncertainty, and active learning queries to learn new concepts encountered during operation. Prior work on dialog systems has either focused on exclusively learning how to perform clarification/ information seeking, or to perform active learning. In this work, we train a hierarchical dialog policy to jointly perform {\it both} clarification and active learning in the context of an interactive language-based image retrieval task motivated by an online shopping application, and demonstrate that jointly learning dialog policies for clarification and active learning is more effective than the use of static dialog policies for one or both of these functions.
NE-Motion: Visual Analysis of Stroke Patients Using Motion Sensor Networks

A large number of stroke survivors suffer from a significant decrease in upper extremity (UE) function, requiring rehabilitation therapy to boost recovery of UE motion. Assessing the efficacy of treatment strategies is a challenging problem in this context, and is typically accomplished by observing the performance of patients during their execution of daily activities. A more detailed assessment of UE impairment can be undertaken with a clinical bedside test, the UE Fugl–Meyer Assessment, but it fails to examine compensatory movements of functioning body segments that are used to bypass impairment. In this work, we use a graph learning method to build a visualization tool tailored to support the analysis of stroke patients. Called NE-Motion, or Network Environment for Motion Capture Data Analysis, the proposed analytic tool handles a set of time series captured by motion sensors worn by patients so as to enable visual analytic resources to identify abnormalities in movement patterns. Developed in close collaboration with domain experts, NE-Motion is capable of uncovering important phenomena, such as compensation while revealing differences between stroke patients and healthy individuals. The effectiveness of NE-Motion is shown in two case studies designed to analyze particular patients and to compare groups of subjects.
Making Flexible Use of Subtasks: A Multiplex Interaction Network for Unified Aspect-based Sentiment Analysis

Aspect Term Extraction (ATE), Opinion Term Extraction (OTE) and Aspect Sentiment Classification (ASC) are the essential building blocks of Aspect-based Sentiment Analysis (ABSA).They are typically treated as separate tasks and are individually studied by previous work.Recent studies intend to incorporate multiple sub-tasks into a unified framework, but suffer from the following major disadvantages: (1) ABSA models are extremely fragile when some sub-tasks are absent; (2) the interactive relations among subtasks are not adequate.To this end, we propose a multi-task learning approach named MIN (Multiplex Interaction Network) to make flexible use of sub-tasks for a unified ABSA.We divide the sub-tasks of ABSA into extractive sub-tasks and classification sub-tasks, and optimize these sub-tasks in a unified manner with multiplex interaction mechanisms.Specifically, we devise a pairwise attention to exploit bidirectional interactions between any arbitrary pair of extractive sub-tasks and a consistency-weighting to perform unidirectional interaction from an extractive sub-task to a classification sub-task.Since the proposed interaction mechanisms are task-agnostic, our model can also work well when some specific sub-tasks are absent.Extensive experiments on two widely used benchmarks with different numbers of sub-tasks demonstrate the superiority of the proposed model.
Gaussian Process based Deep Dyna-Q approach for Dialogue Policy Learning

Applying reinforcement learning to dialogue policy learning requires prohibitively large rounds of human-machine interactions.To improve the learning performance, the Deep Dyna-Q framework with a world model that imitates real users is widely used in recent years.Unfortunately, how to build an effective world model and how to evaluate the experiences generated by the world model efficiently have not been well studied.In order to further improve the effectiveness and efficiency of dialogue policy learning, we present a novel Gaussian Process based Deep Dyna-Q approach in this paper.The Gaussian Process model, which is analytically tractable and fits for small-sample problems, is introduced to build the world model.In addition, we design a highly efficient Kullback-Leibler divergence based discriminator to evaluate the quality of experiences generated by the world model.Extensive experiments validate the effectiveness and robustness of our proposed approach.The task-completion success rate can be improved by about 20% with fewer human-machine interactions.
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization.However, due to the black-box nature of neural models, uninformative evaluation metrics, and scarce tooling for model and data analysis, the true performance and failure modes of summarization models remain largely unknown.To address this limitation, we introduce SUMMVIS, an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of the models, data, and evaluation metrics associated with text summarization.Through its lexical and semantic visualizations, the tools offers an easy entry point for in-depth model prediction exploration across important dimensions such as factual consistency or abstractiveness.The tool together with several pre-computed model outputs is available at https://summvis.com.
Ada-Segment: Automated Multi-loss Adaptation for Panoptic Segmentation

Panoptic segmentation that unifies instance segmentation and semantic segmentation has recently attracted increasing attention. While most existing methods focus on designing novel architectures, we steer toward a different perspective: performing automated multi-loss adaptation (named Ada-Segment) on the fly to flexibly adjust multiple training losses over the course of training using a controller trained to capture the learning dynamics. This offers a few advantages: it bypasses manual tuning of the sensitive loss combination, a decisive factor for panoptic segmentation; allows to explicitly model the learning dynamics, and reconcile the learning of multiple objectives (up to ten in our experiments); with an end-to-end architecture, it generalizes to different datasets without the need of re-tuning hyperparameters or re-adjusting the training process laboriously. Our Ada-Segment brings 2.7% panoptic quality (PQ) improvement on COCO val split from the vanilla baseline, achieving the state-of-the-art 48.5% PQ on COCO test-dev split and 32.9% PQ on ADE20K dataset. The extensive ablation studies reveal the ever-changing dynamics throughout the training process, necessitating the incorporation of an automated and adaptive learning strategy as presented in this paper.
Multimodality for Passive Experience: Effects of Visual, Auditory, Vibration and Draught Stimuli on Sense of Presence

Adequate use of multimodal stimuli plays a crucial role in help forming the sense of presence within a virtual environment. While most of the presence research attempts to engage more sensory modalities to induce a higher sense of presence, this paper investigates the relevance of each sensory modality and different combinations on the subjective sense of presence using a specifically designed scenario of a passive experience. We chose a neutral test scenario of &amp;ldquo;waiting at a train station while a train is passing by&amp;rdquo; to avoid the potential influence of story narrative on mental presence and replicated realistic multimodal stimuli that are highly relevant to our test setting. All four stimuli - visual, auditory, vibration, and draught - with 16 possibilities of combinations were systematically evaluated with 24 participants. The evaluation was performed on one crucial aspect of presence &amp;ndash; &amp;ldquo;realness&amp;rdquo; to reflect user presence in general. The perceived realism value was assessed using a scalometer. The findings of main effects indicate that the auditory stimuli had the most significant contribution in creating the sense of presence. The results of interaction effects suggest the impact of draught stimuli is significant in relation to other stimuli - visual and auditory. Also, the gender effects revealed that the sense of presence reported by female participants is influenced by more factors than merely adding more sensory modalities.