Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hmong Corpus with Elaborate Expression Annotations

This paper describes the first publicly available corpus of Hmong, a minority language of China, Vietnam, Laos, Thailand, and various countries in Europe and the Americas. The corpus has been scraped from a long-running Usenet newsgroup called soc.culture. hmong and consists of approximately 12 million tokens. This corpus (called SCH) is also the first substantial corpus to be annotated for elaborate expressions, a kind of four-part coordinate construction that is common and important in the languages of mainland Southeast Asia. We show that word embeddings trained on SCH can benefit tasks in Hmong (solving analogies) and that a model trained on it can label previously unseen elaborate expressions, in context, with an F1 of 90.79 (precision: 87.36, recall: 94.52). [ISO 639-3: mww, hmj]
Universal Dependencies for Western Sierra Puebla Nahuatl

We present a morpho-syntactically-annotated corpus of Western Sierra Puebla Nahuatl that conforms to the annotation guidelines of the Universal Dependencies project. We describe the sources of the texts that make up the corpus, the annotation process, and important annotation decisions made throughout the development of the corpus. As the first indigenous language of Mexico to be added to the Universal Dependencies project, this corpus offers a good opportunity to test and more clearly define annotation guidelines for the Meso-american linguistic area, spontaneous and elicited spoken data, and code-switching.
E-ConvRec: A Large-Scale Conversational Recommendation Dataset for E-Commerce Customer Service

There has been a growing interest in developing conversational recommendation system (CRS), which provides valuable recommendations to users through conversations. Compared to the traditional recommendation, it advocates wealthier interactions and provides possibilities to obtain users’ exact preferences explicitly. Nevertheless, the corresponding research on this topic is limited due to the lack of broad-coverage dialogue corpus, especially real-world dialogue corpus. To handle this issue and facilitate our exploration, we construct E-ConvRec, an authentic Chinese dialogue dataset consisting of over 25k dialogues and 770k utterances, which contains user profile, product knowledge base (KB), and multiple sequential real conversations between users and recommenders. Next, we explore conversational recommendation in a real scene from multiple facets based on the dataset. Therefore, we particularly design three tasks: user preference recognition, dialogue management, and personalized recommendation. In the light of the three tasks, we establish baseline results on E-ConvRec to facilitate future studies.
Priorities for Robust and Bene fi cial

WINTER 2015 105 Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, the criterion for intelligence is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic representations and statistical learning methods has led to a large degree of integration and crossfertilization between AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems.
TBD3: A Thresholding-Based Dynamic Depression Detection from Social Media for Low-Resource Users

Social media are heavily used by many users to share their mental health concerns and diagnoses. This trend has turned social media into a large-scale resource for researchers focused on detecting mental health conditions. Social media usage varies considerably across individuals. Thus, classification of patterns, including detecting signs of depression, must account for such variation. We address the disparity in classification effectiveness for users with little activity (e.g., new users). Our evaluation, performed on a large-scale dataset, shows considerable detection discrepancy based on user posting frequency. For instance, the F1 detection score of users with an above-median versus below-median number of posts is greater than double (0.803 vs 0.365) using a conventional CNN-based model; similar results were observed on lexical and transformer-based classifiers. To complement this evaluation, we propose a dynamic thresholding technique that adjusts the classifier’s sensitivity as a function of the number of posts a user has. This technique alone reduces the margin between users with many and few posts, on average, by 45% across all methods and increases overall performance, on average, by 33%. These findings emphasize the importance of evaluating and tuning natural language systems for potentially vulnerable populations.
ProQE: Proficiency-wise Quality Estimation dataset for Grammatical Error Correction

This study investigates how supervised quality estimation (QE) models of grammatical error correction (GEC) are affected by the learners’ proficiency with the data. QE models for GEC evaluations in prior work have obtained a high correlation with manual evaluations. However, when functioning in a real-world context, the data used for the reported results have limitations because prior works were biased toward data by learners with relatively high proficiency levels. To address this issue, we created a QE dataset that includes multiple proficiency levels and explored the necessity of performing proficiency-wise evaluation for QE of GEC. Our experiments demonstrated that differences in evaluation dataset proficiency affect the performance of QE models, and proficiency-wise evaluation helps create more robust models.
The Badalona Corpus - An Audio, Video and Neuro-Physiological Conversational Dataset

We present in this paper the first natural conversation corpus recorded with all modalities and neuro-physiological signals. 5 dyads (10 participants) have been recorded three times, during three sessions (30mns each) with 4 days interval. During each session, audio and video are captured as well as the neural signal (EEG with Emotiv-EPOC) and the electro-physiological one (with Empatica-E4). This resource original in several respects. Technically, it is the first one gathering all these types of data in a natural conversation situation. Moreover, the recording of the same dyads at different periods opens the door to new longitudinal investigations such as the evolution of interlocutors’ alignment during the time. The paper situates this new type of resources with in the literature, presents the experimental setup and describes different annotations enriching the corpus.
Evolving Large Text Corpora: Four Versions of the Icelandic Gigaword Corpus

The Icelandic Gigaword Corpus was first published in 2018. Since then new versions have been published annually, containing new texts from additional sources as well as from previous sources. This paper describes the evolution of the corpus in its first four years. All versions are made available under permissive licenses and with each new version the texts are annotated with the latest and most accurate tools. We show how the corpus has grown almost 50% in size from the first version to the fourth and how it was restructured in order to better accommodate different meta-data for different subcorpora. Furthermore, other services have been set up to facilitate usage of the corpus for different use cases. These include a keyword-in-context concordance tool, an n-gram viewer, a word frequency database and pre-trained word embeddings.
Voice disorder and quality of life in teachers: a case-control

Introduction : the individual’s perception of their voice and impacts on their daily lives has been the object of studies that seek the relationship between quality of life and well-being. Objective : to analyze the relationship between quality of life and the presence of voice disorder in teachers from the municipal network of São Paulo. Method : case-control study, paired by school, with 272 teachers from the municipal education network of São Paulo (167 cases and 105 controls) using questionnaires Vocal Production Condition-Teacher, World Health Organization Quality of Life/bref, speech therapy and otorhinolaryngological assessment, test of Chi-square association, logistic regression models to calculate the crude and adjusted Odds Ratio to assess risks in relation to the independent variables of interest. Results : the groups were similar in terms of sociodemographic data, functional situation, work environment and organization, and different in terms of self-reference to vocal symptoms, confirming the nature of a case-control study. In the descriptive analysis of each of the WHOQOL/bref domains, the one referring to the environment had the worst average, followed by the physical, psychological and social relationships. The physical domain was statistically significant when compared to the other domains, followed by the psychological and environmental domains. There was no significant difference on social relationships. Conclusion : there was an association between the presence of voice disorder and impairment of the physical domain of quality of life, with an increase of almost three times the chances of those who have voice disorder to have low scores in that domain.
Development of a Mechanical Strain Device to Prevent Microgravity-Induced Bone Loss

Microgravity-induced bone loss bears similarities to musculoskeletal pathologies such as disuse osteoporosis. The development of mechanical strain devices for astronauts may provide similar benefits as have been demonstrated for postmenopausal women with low bone mass. This communication proposes a hypothetical design and optimization for a strain device, based on the Fourier method, that could potentially used to limit bone loss on mid- to long-term manned space flight missions
Combining Run-Time Checks and Compile-Time Analysis to Improve Control Flow Auto-Vectorization

SIMD (Single Instruction Multiple Data) instructions apply the same operation to multiple elements simultaneously. Compilers transform codes to exploit SIMD instructions through auto-vectorization. Control flow can lead to challenges for auto-vectorization tools because compilers conservatively assume branches are divergent. However, it is common that all SIMD lanes follow the same control-path at run-time, a property we call dynamic uniformity. In this paper, we present VecRC (an auto-vectorizer with run-time checks), a novel compile-time technique that uses run-time checks to test for dynamically uniform control flows. Under the assumption of dynamic uniformity, we perform several compile-time analyses that improve control flow auto-vectorization vs state-of-the-art approaches. VecRC leverages dynamic uniformity to vectorize loops with control-dependent loop-carried dependences. Existing strategies use speculation to optimistically execute vector code, and must correct any incorrect computation due to violated run-time assumptions. VecRC performs compile-time analysis based on uniformity to support such dependences without the overhead of speculation. We propose a probability-based cost model to predict the profitability of run-time checks to eliminate the need for specialized profiling or expensive auto-tuning required in existing methods. VecRC is evaluated in LLVM on a diverse range of benchmarks including SPEC2017, NPB, Parboil, TSVC, and Rodinia on Intel Skylake and IBM Power 9 architectures. On the Skylake architecture, geometric mean speedups of 1.31x, 1.20x, 1.19x, and 1.06x over Region Vectorizer, GCC, Clang, and ICC are obtained with VecRC on real benchmark code.
Distributed Routing Algorithms for Dynamic Geospatial Networks

Typical models of computer networks assume a mostly-fixed topology, where the underlying connectivity of the nodes is largely unchanged, and routing tables can be reasonably maintained. However, the advent of short-range wireless technologies and internet-of-things devices has led to the emergence of dynamic networks, where the network topology changes frequently enough that maintaining routing tables is infeasible. We consider several possible mathematical models of dynamic network topologies, propose several routing algorithms for efficient message delivery, and empirically evaluate and visualize these algorithms on a custom-built simulator. (Final project for Stanford’s CS 244B.)
Public Transport Investments, Commuting and Gentrification: Evidence from Copenhagen

This paper considers the impact of the introduction of a metro network in the Copenhagen metropolitan area. Using travel surveys from years before and after the opening of the metro network, we observe a significant change in travel times, speeds and mode choice for commutes that can completely or partly be realized by the metro. Interest in the metro among the higher educated is much stronger than among the lower educated. House prices in the vicinity of the metro stations increased significantly. The total additional value of real estate generated by the metro is appr. 40% of the actual construction cost. The government captured a substantial part of the value generated by the metro by concentrating housing construction in some hitherto undeveloped areas close to metro stations. We use a gravity model to explore the implications of the metro for urban structure in an urban equilibrium context and find that all adjustment takes place in the housing market. The lower and medium educated face adjustments in housing attractiveness that counteract the initial impact of the metro. We find no evidence for such adverse effects on the higher educated, which suggest a close connection between the impact of the metro and gentrification in the Copenhagen. and high education includes: bachelor, long-cycle higher education and PhD-degree.
CAMS: An Annotated Corpus for Causal Analysis of Mental Health Issues in Social Media Posts

The social NLP researchers and mental health practitioners have witnessed exponential growth in the field of mental health detection and analysis on social media. It has become important to identify the reason behind mental illness. In this context, we introduce a new dataset for Causal Analysis of Mental health in Social media posts (CAMS). We first introduce the annotation schema for this task of causal analysis. The causal analysis comprises of two types of annotations, viz, causal interpretation and causal categorization. We show the efficacy of our scheme in two ways: (i) crawling and annotating 3155 Reddit data and (ii) re-annotate the publicly available SDCNL dataset of 1896 instances for interpretable causal analysis. We further combine them as CAMS dataset and make it available along with the other source codes https://anonymous.4open. science/r/CAMS1/. Our experimental results show that the hybrid CNN-LSTM model gives the best performance over CAMS dataset.
Visualization and Interpretation of Protein Interactions

Visualization and interpretation of deep learning models’ prediction is a very important area of research in machine learning nowadays. Researchers are not only focused on generating a model with good performance, but also they want to trust the model. Our aim in this thesis is to adapt existing interpretation methods to a protein-protein binding site prediction problem to visualize and understand the model’s prediction and learning pattern. We present three deep learning-based interpretation methods: sensitivity analysis, saliency map and integrated gradients to analyze the amino acid residues which create positive and negative relevance to the deep learning models’ prediction. As our applications use sliding window protocol, we are particularly interested in learning patterns of the model and identify the important positions. Also, we want to focus on the feature importance through Local Interpretable Model-Agnostic Explanations (LIME). With our experiment we observe that, in spite of using various features, position specific scoring matrices (PSSM) is identified as the most important feature that helps the model to identify positive classes. As PSSM’s importance is proven historically, this finding gives us a strong biological significance as well.
Saliency-Regularized Deep Multi-Task Learning

Multi-task learning (MTL) is a framework that enforces multiple learning tasks to share their knowledge to improve their generalization abilities. While shallow multi-task learning can learn task relations, it can only handle pre-defined features. Modern deep multi-task learning can jointly learn latent features and task sharing, but they are obscure in task relation. Also, they pre-define which layers and neurons should share across tasks and cannot learn adaptively. To address these challenges, this paper proposes a new multi-task learning framework that jointly learns latent features and explicit task relations by complementing the strength of existing shallow and deep multitask learning scenarios. Specifically, we propose to model the task relation as the similarity between tasks' input gradients, with a theoretical analysis of their equivalency. In addition, we innovatively propose a multi-task learning objective that explicitly learns task relations by a new regularizer. Theoretical analysis shows that the generalizability error has been reduced thanks to the proposed regularizer. Extensive experiments on several multi-task learning and image classification benchmarks demonstrate the proposed method's effectiveness, efficiency as well as reasonableness in the learned task relation patterns.
Telling a Lie: Analyzing the Language of Information and Misinformation during Global Health Events

The COVID-19 pandemic and other global health events are unfortunately excellent environments for the creation and spread of misinformation, and the language associated with health misinformation may be typified by unique patterns and linguistic markers. Allowing health misinformation to spread unchecked can have devastating ripple effects; however, detecting and stopping its spread requires careful analysis of these linguistic characteristics at scale. We analyze prior investigations focusing on health misinformation, associated datasets, and detection of misinformation during health crises. We also introduce a novel dataset designed for analyzing such phenomena, comprised of 2.8 million news articles and social media posts spanning the early 1900s to the present. Our annotation guidelines result in strong agreement between independent annotators. We describe our methods for collecting this data and follow this with a thorough analysis of the themes and linguistic features that appear in information versus misinformation. Finally, we demonstrate a proof-of-concept misinformation detection task to establish dataset validity, achieving a strong performance benchmark (accuracy = 75%; F1 = 0.7).
User Interest Modelling in Argumentative Dialogue Systems

Most systems helping to provide structured information and support opinion building, discuss with users without considering their individual interest. The scarce existing research on user interest in dialogue systems depends on explicit user feedback. Such systems require user responses that are not content-related and thus, tend to disturb the dialogue flow. In this paper, we present a novel model for implicitly estimating user interest during argumentative dialogues based on semantically clustered data. Therefore, an online user study was conducted to acquire training data which was used to train a binary neural network classifier in order to predict whether or not users are still interested in the content of the ongoing dialogue. We achieved a classification accuracy of 74.9% and furthermore investigated with different Artificial Neural Networks (ANN) which new argument would fit the user interest best.
Abyssal vent field habitats along plate margins in the Central Indian Ocean yield new species in the genus Anatoma (Vetigastropoda: Anatomidae)

New species in Anatoma Woodward, 1859 (Anatomidae, Vetigastropoda) are found in abyssal hydrothermal vent field habitats on the oceanic plate margins in the Indian Ocean. Six species are identified using molecular sequence analyses of which four species are described as new based on their morphological characters: Anatoma discapex sp. nov., Anatoma declivis sp. nov., Anatoma laevapex sp. nov. and Anatoma paucisculpta sp. nov. Inadequate material was available for a morphological description of the other two species with genetic identification, but it is likely that all six species are new to science and endemic to the Indian Ocean. The northern Central Indian Ridge localities are dominated by Anatoma declivis sp. nov.; its closest relative is Anatoma discapex sp. nov. which occurs in the central area near the Rodriguez Triple Junction. Anatoma laevapex sp. nov. and Anatoma paucisculpta sp. nov. as well as a fifth undescribed species are mainly found on the Southeast Indian Ridge.
Influence of Stator MMF Harmonics on the Utilization of Reluctance Torque in Six-Phase PMA-SynRM with FSCW

Although fractional-slot concentrated winding (FSCW) offers many significant advantages, such as short end-turn windings, high slot filling factor, and low cogging torque, it is frequently limited by excessive stator magnetomotive force (MMF) harmonics which will induce high eddy losses in the permanent magnets (PMs). What is more, in the literature, it can be observed that the reluctance torque of the salient-pole machine with FSCW is usually much lower than that obtained with integral slot winding. To explore the underlying reason why the reluctance torque in a salient machine with FSCW significantly decreases, a new six-phase FSCW with 24 slots and 10 poles, which can significantly reduce the undesirable stator MMF harmonics, is obtained by using the concept of stator shifting. Then, two permanent-magnet-assisted synchronous reluctance machines (PMA-SynRMs) with the proposed winding layout and conventional asymmetric 12-slot/10-pole six-phase winding layout are designed and simulated by the finite-element method (FEM). The reluctance torque, total torque, and d/q-axis inductances with different current phase angles are also compared under different loaded conditions. The results show that a reduction in stator MMF harmonics can indeed lead to a significant enhancement in reluctance torque under heavy loaded conditions, while the dominance will diminish under light loaded conditions.
Part-Load Simulation and Energy Recovery Evaluation of an Electrically Turbocharged Engine Low Engine Speeds

Hybridization has become a crucial part of engine development for automotive manufacturers nowadays to produce efficient engines and electric turbochargers have become the center-piece of that development to cut carbon emissions. In this paper, we simulate an electrically turbocharged engine under part-load using a 1-D engine simulation software to estimate the amount of energy that can be recovered at different engine loads in a typical passenger vehicle. A conventional turbocharged engine and an electric turbocharged engine are first simulated under steady part load conditions with fixed target Brake Torque (Nm). Then the electric turbocharged engine is simulated to run at points between 1,000-2,000 rpm engine range to determine the amount of power recovered at low engine speed. From this study, the BSFC increases by 1.3% at 50% engine load at 4,000 rpm in the electrically turbocharged engine over conventional turbocharged engine whereas at 5,000 rpm it decreases by 2.4% at 75% engine load. A maximum of 3.22 kW was able to be recovered at 5,000 rpm 50% load and 1.5kW at low engine speeds.