Reviewerly/exHarmony huggingface dataset:
Seismic Surveillance and Earthquake Monitoring in Italy

Abstract The Istituto Nazionale di Geofisica e Vulcanologia (INGV) is an Italian research institution with focus on earth sciences. Moreover, the INGV is the operational center for seismic surveillance and earthquake monitoring in Italy and is a part of the civil protection system as a center of expertise on seismic, volcanic, and tsunami risks.INGV operates the Italian National Seismic Network and other networks at national scale and is a primary node of the European Integrated Data Archive for archiving and distributing strong-motion and weak-motion seismic recordings. In the control room in Rome, INGV staff performs seismic surveillance and tsunami warning services; in Catania and Naples, the control rooms are devoted to volcanic surveillance. Volcano monitoring includes locating earthquakes in the regions around the Sicilian (Etna, Eolian Islands, and Pantelleria) and the Campanian (Vesuvius, Campi Fregrei, and Ischia) active volcanoes. The tsunami warning is based on earthquake location and magnitude (M) evaluation for moderate to large events in the Mediterranean region and also around the world. The technologists of the institute tuned the data acquisition system to accomplish, in near real time, automatic earthquake detection, hypocenter and magnitude determination, and evaluation of several seismological products (e.g., moment tensors and ShakeMaps). Database archiving of all parametric results is closely linked to the existing procedures of the INGV seismic surveillance environment and surveillance procedures. Earthquake information is routinely revised by the analysts of the Italian seismic bulletin. INGV provides earthquake information to the Department of Civil Protection (Dipartimento di Protezione Civile) to the scientific community and to the public through the web and social media. We aim at illustrating different aspects of earthquake monitoring at INGV: (1) network operations; (2) organizational structure and the hardware and software used; and (3) communication, including recent developments and planned improvements.
Dual Attentive Sequential Learning for Cross-Domain Click-Through Rate Prediction

Cross domain recommender system constitutes a powerful method to tackle the cold-start and sparsity problem by aggregating and transferring user preferences across multiple category domains. Therefore, it has great potential to improve click-through-rate prediction performance in online commerce platforms having many domains of products. While several cross domain sequential recommendation models have been proposed to leverage information from a source domain to improve CTR predictions in a target domain, they did not take into account bidirectional latent relations of user preferences across source-target domain pairs. As such, they cannot provide enhanced cross-domain CTR predictions for both domains simultaneously. In this paper, we propose a novel approach to cross-domain sequential recommendations based on the dual learning mechanism that simultaneously transfers information between two related domains in an iterative manner until the learning process stabilizes. In particular, the proposed Dual Attentive Sequential Learning (DASL) model consists of two novel components Dual Embedding and Dual Attention, which jointly establish the two-stage learning process: we first construct dual latent embeddings that extract user preferences in both domains simultaneously, and subsequently provide cross-domain recommendations by matching the extracted latent embeddings with candidate items through dual-attention learning mechanism. We conduct extensive offline experiments on three real-world datasets to demonstrate the superiority of our proposed model, which significantly and consistently outperforms several state-of-the-art baselines across all experimental settings. We also conduct an online A/B test at a major video streaming platform Alibaba-Youku, where our proposed model significantly improves business performance over the latest production system in the company.
ESC: Redesigning WSD with Extractive Sense Comprehension

Word Sense Disambiguation (WSD) is a historical NLP task aimed at linking words in contexts to discrete sense inventories and it is usually cast as a multi-label classification task.Recently, several neural approaches have employed sense definitions to better represent word meanings.Yet, these approaches do not observe the input sentence and the sense definition candidates all at once, thus potentially reducing the model performance and generalization power.We cope with this issue by reframing WSD as a span extraction problem -which we called Extractive Sense Comprehension (ESC) -and propose ESCHER, a transformer-based neural architecture for this new formulation.By means of an extensive array of experiments, we show that ESC unleashes the full potential of our model, leading it to outdo all of its competitors and to set a new state of the art on the English WSD task.In the few-shot scenario, ESCHER proves to exploit training data efficiently, attaining the same performance as its closest competitor while relying on almost three times fewer annotations.Furthermore, ESCHER can nimbly combine data annotated with senses from different lexical resources, achieving performances that were previously out of everyone's reach.The model along with data is available at https://github.com/SapienzaNLP/esc.
LoBSTr: Real‐time Lower‐body Pose Prediction from Sparse Upper‐body Tracking Signals

Abstract With the popularization of games and VR/AR devices, there is a growing need for capturing human motion with a sparse set of tracking data. In this paper, we introduce a deep neural network (DNN) based method for real‐time prediction of the lower‐body pose only from the tracking signals of the upper‐body joints. Specifically, our Gated Recurrent Unit (GRU)‐based recurrent architecture predicts the lower‐body pose and feet contact states from a past sequence of tracking signals of the head, hands, and pelvis. A major feature of our method is that the input signal is represented by the velocity of tracking signals. We show that the velocity representation better models the correlation between the upper‐body and lower‐body motions and increases the robustness against the diverse scales and proportions of the user body than position‐orientation representations. In addition, to remove foot‐skating and floating artifacts, our network predicts feet contact state, which is used to post‐process the lower‐body pose with inverse kinematics to preserve the contact. Our network is lightweight so as to run in real‐time applications. We show the effectiveness of our method through several quantitative evaluations against other architectures and input representations with respect to wild tracking data obtained from commercial VR devices.
Fine-grained Post-training for Improving Retrieval-based Dialogue Systems

Retrieval-based dialogue systems display an outstanding performance when pre-trained language models are used, which includes bidirectional encoder representations from transformers (BERT).During the multi-turn response selection, BERT focuses on training the relationship between the context with multiple utterances and the response.However, this method of training is insufficient when considering the relations between each utterance in the context.This leads to a problem of not completely understanding the context flow that is required to select a response.To address this issue, we propose a new fine-grained post-training method that reflects the characteristics of the multi-turn dialogue.Specifically, the model learns the utterance level interactions by training every short context-response pair in a dialogue session.Furthermore, by using a new training objective, the utterance relevance classification, the model understands the semantic relevance and coherence between the dialogue utterances.Experimental results show that our model achieves new state-of-the-art with significant margins on three benchmark datasets.This suggests that the fine-grained post-training method is highly effective for the response selection task. 1
Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax

Currently the unified semantic role labeling (SRL) that achieves predicate identification and argument role labeling in an end-to-end manner has received growing interests. Recent works show that leveraging the syntax knowledge significantly enhances the SRL performances. In this paper, we investigate a novel unified SRL framework based on the sequence-to-sequence architecture with double enhancement in both the encoder and decoder sides. In the encoder side, we propose a novel label-aware graph convolutional network (LA-GCN) to encode both the syntactic dependent arcs and labels into BERT-based word representations. In the decoder side, we creatively design a pointer-network-based model for detecting predicates, arguments and roles jointly. Our pointer-net decoder is able to make decisions by consulting all the input elements in a global view, and meanwhile it is syntactic-aware by incorporating the syntax information from LA-GCN. Besides, a high-order interacted attention is introduced into the decoder for leveraging previously recognized triplets to help the current decision. Empirical experiments show that our framework significantly outperforms all existing graph-based methods on the CoNLL09 and Universal Proposition Bank datasets. In-depth analysis demonstrates that our model can effectively capture the correlations between syntactic and SRL structures.
SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging

Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce the performance of models trained using federated learning. In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning. Unlike previous federated methods that require complex heuristic training or hyper parameter tuning, our SplitAVG leverages the simple network split and feature map concatenation strategies to encourage the federated model training an unbiased estimator of the target data distribution. We compare SplitAVG with seven state-of-the-art federated learning methods, using centrally hosted training data as the baseline on a suite of both synthetic and real-world federated datasets. We find that the performance of models trained using all the comparison federated learning methods degraded significantly with the increasing degrees of data heterogeneity. In contrast, SplitAVG method achieves comparable results to the baseline method under all heterogeneous settings, that it achieves 96.2% of the accuracy and 110.4% of the mean absolute error obtained by the baseline in a diabetic retinopathy binary classification dataset and a bone age prediction dataset, respectively, on highly heterogeneous data partitions. We conclude that SplitAVG method can effectively overcome the performance drops from variability in data distributions across institutions. Experimental results also show that SplitAVG can be adapted to different base convolutional neural networks (CNNs) and generalized to various types of medical imaging tasks. The code is publicly available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/zm17943/SplitAVG</uri> .
Understanding the Robustness of Skeleton-based Action Recognition under Adversarial Attack

Action recognition has been heavily employed in many applications such as autonomous vehicles, surveillance, etc, where its robustness is a primary concern. In this paper, we examine the robustness of state-of-the-art action recognizers against adversarial attack, which has been rarely investigated so far. To this end, we propose a new method to attack action recognizers which rely on the 3D skeletal motion. Our method involves an innovative perceptual loss which ensures the imperceptibility of the attack. Empirical studies demonstrate that our method is effective in both white-box and black-box scenarios. Its generalizability is evidenced on a variety of action recognizers and datasets. Its versatility is shown in different attacking strategies. Its deceitfulness is proven in extensive perceptual studies. Our method shows that adversarial attack on 3D skeletal motions, one type of time-series data, is significantly different from traditional adversarial attack problems. Its success raises serious concern on the robustness of action recognizers and provides insights on potential improvements.
Post-epidemic factors influencing customer's booking intent for a hotel or leisure spot: an empirical study

Purpose The new coronavirus is a highly infectious disease with mutating variants leading to pervasive risk around geographies and public health system. The economy has been suffering due to the strategic lockdown adopted by the local administrative bodies, and in most of the countries, it is further leading to a major wave of unemployment with millions of job and business losses affecting the hotels, travel and tourism industry widely. To attain a sustainable business in the post-pandemic situations, the industry now must think of information system approaches to convince tourists to feel safe with the most hygienic hospitality and services to be offered in any property. The key aspect of the study is to provide the impact of new-age AI-driven technology solutions that will dominate the future direction of the modernized hospitality industry promising robust health-safety measures in a hotel, and further help create sustainable business and leisure travel facilities to cope with post-epidemic scenarios. Design/methodology/approach The study emphasizes to provide a robust technology-oriented framework based on a mixed research method that would help hotels to adopt and implement new-age AI-driven solution within the hotel premise to serve customers with at most hygiene, contactless service and thereafter, aiming for faster recovery of businesses and regaining customer trust to fuel booking intent in the post-epidemic scenario. Findings The paper provides a technology-focused solution that would impact hotel industries' post-pandemic scenario. The study contributes to helping boost the tourism industry using information management solutions such as biosensors, robotic room services and contactless hosting. The findings show the adoption of robots/RPA solutions and Biosensors by the industry will be a disruptive paradigm shift. Originality/value The study expands the scope of research in information technology and management with a focus on the hospitality industry while contributing to new factors impacting customer buying behavior in the industry.
Formation of a Competency Model in Teaching Students of Technical Universities with Hearing Impairment, which Implements a Conveyor-Based Approach to Learning

At that moment, modern pedagogy is faced with the need to ensure high-quality and affordable higher technical education in the face of new challenges (the need to obtain it through remote technologies; the growing shortage of personnel among the teaching staff; the need to implement a conveyor- based learning approach to implement the principles of system analysis in teaching, when at the end of teachning process a group of scpecialist led by a leader should be obtained) The last of these has achieved a significant reduction of time required for graduates in the labor life of the enterprise. It should be noted that these challenges are most pronounced in the preparation of specialists with a higher technical education from students with hearing impairments. This paper is particular answer to the challenges posed above. Thus, the paper proposes an algorithm for dynamic adaptation of the educational process, presented in the form of a competency-based model of teaching students with hearing impairments. The core of the algorithm is methods for estimating the quality of publications and textbooks.
Online and Offline Mixed Intelligent Teaching Assistant Mode of English Based on Mobile Information System

In order to actively explore the English teaching methods in the “Internet+” era, by exploring the teaching effects of online and offline mixed teaching English courses, it is found that the online and offline mixed teaching methods are very popular among students. Autonomous learning can enable students to acquire knowledge with greater flexibility and efficiency and make teacher guidance more targeted and interactive in offline classrooms, thereby improving teaching effects. In the new era, the rapid development and progress of Internet technology, information technology, and multimedia provide a good opportunity for China to continue to promote the reform of education and teaching. This article aims to study the online and offline mixed intelligent teaching assistant mode of English based on the mobile information system, expounds the overall design strategy of the online and offline mixed teaching mode, and proposes to strengthen the online and offline English mixed intelligent teaching assistant mode. And this article proposes an English hybrid intelligent teaching assistant model based on mobile information system development, which integrates massive English teaching resources. According to each student’s learning progress, tailor-made online learning courses and materials, combined with offline auxiliary teaching, achieve double education guarantee. The experimental results in this article show that if 30 people in the exam refer to the results, the teacher wants to analyze the results of these 10 questions. Please enter these 10 questions and corresponding rules of the user interface into the database, and use the system for reasoning and analysis, and the knowledge points involved in selecting 10 questions on the test paper are divided into 9 different grammatical points. When the entire class has conquered them and they are all deterministic answers, the weighted value is the current lowest value, which is 0. This shows that the system is very convenient.
The Dutch Data Warehouse, a multicenter and full-admission electronic health records database for critically ill COVID-19 patients

The Coronavirus disease 2019 (COVID-19) pandemic has underlined the urgent need for reliable, multicenter, and full-admission intensive care data to advance our understanding of the course of the disease and investigate potential treatment strategies. In this study, we present the Dutch Data Warehouse (DDW), the first multicenter electronic health record (EHR) database with full-admission data from critically ill COVID-19 patients.A nation-wide data sharing collaboration was launched at the beginning of the pandemic in March 2020. All hospitals in the Netherlands were asked to participate and share pseudonymized EHR data from adult critically ill COVID-19 patients. Data included patient demographics, clinical observations, administered medication, laboratory determinations, and data from vital sign monitors and life support devices. Data sharing agreements were signed with participating hospitals before any data transfers took place. Data were extracted from the local EHRs with prespecified queries and combined into a staging dataset through an extract-transform-load (ETL) pipeline. In the consecutive processing pipeline, data were mapped to a common concept vocabulary and enriched with derived concepts. Data validation was a continuous process throughout the project. All participating hospitals have access to the DDW. Within legal and ethical boundaries, data are available to clinicians and researchers.Out of the 81 intensive care units in the Netherlands, 66 participated in the collaboration, 47 have signed the data sharing agreement, and 35 have shared their data. Data from 25 hospitals have passed through the ETL and processing pipeline. Currently, 3464 patients are included in the DDW, both from wave 1 and wave 2 in the Netherlands. More than 200 million clinical data points are available. Overall ICU mortality was 24.4%. Respiratory and hemodynamic parameters were most frequently measured throughout a patient's stay. For each patient, all administered medication and their daily fluid balance were available. Missing data are reported for each descriptive.In this study, we show that EHR data from critically ill COVID-19 patients may be lawfully collected and can be combined into a data warehouse. These initiatives are indispensable to advance medical data science in the field of intensive care medicine.
The impact of modern technology in the teaching and learning process

The advancement of technology has had an influence on every part of our lives, from banking to the way we connect with one another. Indeed, technology has become an essential component of sustaining civilization, and its incorporation into education is consequently unavoidable. Technology not only gives students access to a plethora of online materials, but it also helps them study. The majority of colleges and educational institutions have already begun to use technology into their teaching techniques. This paper provides in-depth on the effect and impact of the modern technology in the teaching and learning process through reviewing various secondary data. Education has been transformed by technological advancements. The significance of technology in classrooms cannot be overstated. Indeed, the introduction of computers into education has made it simpler for instructors to transfer information and for pupils to retrieve it. The integration of technology into education ecosystem has made the and learning processes more entertaining.
Multi-Target Adversarial Frameworks for Domain Adaptation in Semantic Segmentation

In this work, we address the task of unsupervised domain adaptation (UDA) for semantic segmentation in presence of multiple target domains: The objective is to train a single model that can handle all these domains at test time. Such a multi-target adaptation is crucial for a variety of scenarios that real-world autonomous systems must handle. It is a challenging setup since one faces not only the domain gap between the labeled source set and the un-labeled target set, but also the distribution shifts existing within the latter among the different target domains. To this end, we introduce two adversarial frameworks: (i) multi-discriminator, which explicitly aligns each target domain to its counterparts, and (ii) multi-target knowledge transfer, which learns a target-agnostic model thanks to a multi-teacher/single-student distillation mechanism. The evaluation is done on four newly-proposed multi-target bench-marks for UDA in semantic segmentation. In all tested scenarios, our approaches consistently outperform baselines, setting competitive standards for the novel task.