Reviewerly/exHarmony huggingface dataset:
Application of Propofol in Oral and Maxillofacial Surgery Anesthesia Based on Smart Medical Blockchain Technology

The rapid development of intelligent computer technology has promoted the improvement of people's living standards and the application of the entire intelligent system. In modern medicine, doctors use their accumulated experience and medical knowledge to diagnose diseases and draw conclusions. In order to effectively inherit the diagnosis experience accumulated by doctors, R&D personnel put forward the idea of using artificial intelligence technology to develop an intelligent auxiliary medical diagnosis system. Aiming at the abovementioned machine-learning problems in the medical field, this article mainly introduces the application research of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology. This paper proposes a research method based on smart medical blockchain technology to assist propofol in oral and maxillofacial surgery anesthesia, including support vector machine data classification algorithm, decision tree data classification algorithm, and machine-learning-based LSTM neural network. Research experiments on the application of intelligent medical aid propofol in oral and maxillofacial surgery anesthesia are conducted. The experimental results in this paper show that the response time of the general business logic interface of the application system of propofol in oral and maxillofacial surgery anesthesia based on smart medical blockchain technology is about 41 milliseconds, which can better help doctors in anesthesia and related treatments.
COVID-19 face mask detection in a crowd using multi-model based on YOLOv3 and hand-crafted features

Face recognition is one of the most challenging biometric modalities when deployed in unconstrained environments due to the high variability that faces images present in the real world in a crowd, which are affected by complex factors including head poses, aging, illumination conditions, occlusions, and facial expressions. The face recognition system aims to identify and track visual data subjects, such as images and videos. More people are currently carrying masks in public, bringing new challenges to the face detection and identification system. This article focuses on the detection and recognition of masked faces. The presented framework is based on new artificial intelligence tools that use hand-crafted and deep learning (YOLOv3 and CNNs) features and SVM classifiers. Computer simulation on five different face mask datasets (Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), Medical Mask Dataset(MMD), Labeled Faces in the Wild (LFW)) and our proposed Artificially simulated masked face dataset (ASMFD), of which the testing results illustrate that the proposed method is comparable or better in most cases than traditional face mask recognition techniques. The presented system may produce anonymous statistical data that can help the agencies predict potential epidemics of COVID-19.
David and Goliath Revisited:How small investors are changing the landscape of financial markets.

On January 12, 2021, a group of small investors coordinated a series of trades over the course of a few weeks in an effort to affect the positions of large hedge funds who short stocks. These large hedge funds were unaware of the tsunami that would overtake their short positions causing losses in the billions of dollars. A number of economic theories can provide insight into some of the activities of buyers; however, the retaliatory aspect of these small investors is new. The aim of this paper is to explore these actions using classical economics, behavioral economics and social coordination. Our aim is to collect and analyze stock market activity for stocks such as GameStop, AMC, KOSS, and Nokia, then retrieve the posts from Reddit to determine whether or not these activities could be predicted, as well as determine when acritical mass was obtained to successfully execute these stock trades.
TraceAll: A Real-Time Processing for Contact Tracing Using Indoor Trajectories

The rapid spread of infectious diseases is a major public health problem. Recent developments in fighting these diseases have heightened the need for a contact tracing process. Contact tracing can be considered an ideal method for controlling the transmission of infectious diseases. The result of the contact tracing process is performing diagnostic tests, treating for suspected cases or self-isolation, and then treating for infected persons; this eventually results in limiting the spread of diseases. This paper proposes a technique named TraceAll that traces all contacts exposed to the infected patient and produces a list of these contacts to be considered potentially infected patients. Initially, it considers the infected patient as the querying user and starts to fetch the contacts exposed to him. Secondly, it obtains all the trajectories that belong to the objects moved nearby the querying user. Next, it investigates these trajectories by considering the social distance and exposure period to identify if these objects have become infected or not. The experimental evaluation of the proposed technique with real data sets illustrates the effectiveness of this solution. Comparative analysis experiments confirm that TraceAll outperforms baseline methods by 40% regarding the efficiency of answering contact tracing queries.
Deconstructing Categorization in Visualization Recommendation: A Taxonomy and Comparative Study

Visualization recommendation (VisRec) systems provide users with suggestions for potentially interesting and useful next steps during exploratory data analysis. These recommendations are typically organized into categories based on their analytical actions, i.e., operations employed to transition from the current exploration state to a recommended visualization. However, despite the emergence of a plethora of VisRec systems in recent work, the utility of the categories employed by these systems in analytical workflows has not been systematically investigated. Our article explores the efficacy of recommendation categories by formalizing a taxonomy of common categories and developing a system, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frontier</i> , that implements these categories. Using <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Frontier</i> , we evaluate workflow strategies adopted by users and how categories influence those strategies. Participants found recommendations that add attributes to enhance the current visualization and recommendations that filter to sub-populations to be comparatively most useful during data exploration. Our findings pave the way for next-generation VisRec systems that are adaptive and personalized via carefully chosen, effective recommendation categories.
On combining acoustic and modulation spectrograms in an attention LSTM-based system for speech intelligibility level classification

Speech intelligibility can be affected by multiple factors, such as noisy environments, channel distortions or physiological issues. In this work, we deal with the problem of automatic prediction of the speech intelligibility level in this latter case. Starting from our previous work, a non-intrusive system based on LSTM networks with attention mechanism designed for this task, we present two main contributions. In the first one, it is proposed the use of per-frame modulation spectrograms as input features, instead of compact representations derived from them that discard important temporal information. In the second one, two different strategies for the combination of per-frame acoustic log-mel and modulation spectrograms into the LSTM framework are explored: at decision level or late fusion and at utterance level or Weighted-Pooling (WP) fusion. The proposed models are evaluated with the UA-Speech database that contains dysarthric speech with different degrees of severity. On the one hand, results show that attentional LSTM networks are able to adequately modeling the modulation spectrograms sequences producing similar classification rates as in the case of log-mel spectrograms. On the other hand, both combination strategies, late and WP fusion, outperform the single-feature systems, suggesting that per-frame log-mel and modulation spectrograms carry complementary information for the task of speech intelligibility prediction, than can be effectively exploited by the LSTM-based architectures, being the system with the WP fusion strategy and Attention-Pooling the one that achieves best results.
Monitoring Cyber SentiHate Social Behavior During COVID-19 Pandemic in North America

With communications being shifted to online social networks (OSNs) as a result of travel and social restrictions during COVID-19 pandemic, the need has arisen for discovering emerging trends and concerns formed during the pandemic as well as understanding the corresponding online social behavior that reflects its offline settings.The online connectivity of devices through social media is one example of Internet of Things (IoT) in which a two-way communication between societies and officials, could be created.Therefore, it is possible to monitor people's behavior through OSNs, especially during pandemics, to prevent potential social and psychological instabilities that might lead to undesired consequences.This is particularly crucial for governmental and non-governmental organizations to ensure the stability and well-being in societies.In response, we propose a pandemic-friendly real-time framework for monitoring cyber social behavior by utilizing unsupervised and supervised learning approaches.Two BERT-based supervised classifiers are trained and constructed to analyze two types of online social behaviors, hate and sentiment.Unsupervised framework is proposed for OSNs data exploration and coherent interpretation that is used as a complementary tool to facilitate the analysis of online social behaviors during pandemics.Extensive experimentation and evaluation have been conducted to validate the effectiveness of the proposed work.Our results have shown superior performance of our BERT-based models in two classification tasks: 1) binary classification for hate behavior detection and 2) multi-class classification for sentiment behavior detection.In addition to our experimentation results, our large-scale analysis of COVID-19 pandemic has illustrated the capability of our unsupervised framework for concerns and trends discoveries using OSNs data, along with reliability in automatically and dynamically providing phrase-based interpenetration of the inferred trends and concerns.This paper provides a twelve-month comparison analysis of data discoveries and online social behavior between Canada and USA during COVID-19 pandemic.
TR-BERT: Dynamic Token Reduction for Accelerating BERT Inference

Existing pre-trained language models (PLMs) are often computationally expensive in inference, making them impractical in various resource-limited real-world applications.To address this issue, we propose a dynamic token reduction approach to accelerate PLMs' inference, named TR-BERT, which could flexibly adapt the layer number of each token in inference to avoid redundant calculation.Specially, TR-BERT formulates the token reduction process as a multi-step token selection problem and automatically learns the selection strategy via reinforcement learning.The experimental results on several downstream NLP tasks show that TR-BERT is able to speed up BERT by 2-5 times to satisfy various performance demands.Moreover, TR-BERT can also achieve better performance with less computation in a suite of long-text tasks since its token-level layer number adaption greatly accelerates the self-attention operation in PLMs.
FallDeF5: A Fall Detection Framework Using 5G-Based Deep Gated Recurrent Unit Networks

Fall prevalence is high among elderly people, which is challenging due to the severe consequences of falling. This is why rapid assistance is a critical task. Ambient assisted living (AAL) uses recent technologies such as 5G networks and the internet of medical things (IoMT) to address this research area. Edge computing can reduce the cost of cloud communication, including high latency and bandwidth use, by moving conventional healthcare services and applications closer to end-users. Artificial intelligence (AI) techniques such as deep learning (DL) have been used recently for automatic fall detection, as well as supporting healthcare services. However, DL requires a vast amount of data and substantial processing power to improve its performance for the IoMT linked to the traditional edge computing environment. This research proposes an effective fall detection framework based on DL algorithms and mobile edge computing (MEC) within 5G wireless networks, the aim being to empower IoMT-based healthcare applications. We also propose the use of a deep gated recurrent unit (DGRU) neural network to improve the accuracy of existing DL-based fall detection methods. DGRU has the advantage of dealing with time-series IoMT data, and it can reduce the number of parameters and avoid the vanishing gradient problem. The experimental results on two public datasets show that the DGRU model of the proposed framework achieves higher accuracy rates compared to the current related works on the same datasets.
Memes, Radicalisation, and the Promotion of Violence on Chan Sites

A number of violent far-right attacks in recent years have revealed an apparent connection with `chan culture', not just in the tangible examples of attackers uploading manifestos, final messages and livestreams to chan sites themselves, but in the widespread community support exhibited in some corners of this online subculture where violence is both trivialised and glorified. Commonly, this is manifested in the visual culture present on chan sites, particularly memes, which may be used to promote extreme or even violent narratives under the guise of humour and irony. This paper seeks to understand how the visual culture of chan sites contribute to, and/or encourage violent discourse. In particular, we combine quantitative data scraping, ethnography and visual analysis across tens of chan sites ranging in popularity like 4chan, 8kun, or neinchan between March -- June 2020. Over all we collect a dataset of 135K images across different chans and provide first qualitivative characterization of the most popular images shared across different chans.
Uncertain Interval Forecasting for Combined Electricity-Heat-Cooling-Gas Loads in the Integrated Energy System Based on Multi-Task Learning and Multi-Kernel Extreme Learning Machine

The accurate prediction of electricity-heat-cooling-gas loads on the demand side in the integrated energy system (IES) can provide significant reference for multiple energy planning and stable operation of the IES. This paper combines the multi-task learning (MTL) method, the Bootstrap method, the improved Salp Swarm Algorithm (ISSA) and the multi-kernel extreme learning machine (MKELM) method to establish the uncertain interval prediction model of electricity-heat-cooling-gas loads. The ISSA introduces the dynamic inertia weight and chaotic local searching mechanism into the basic SSA to improve the searching speed and avoid falling into local optimum. The MKELM model is established by combining the RBF kernel function and the Poly kernel function to integrate the superior learning ability and generalization ability of the two functions. Based on the established model, weather, calendar information, social–economic factors, and historical load are selected as the input variables. Through empirical analysis and comparison discussion, we can obtain: (1) the prediction results of workday are better than those on holiday. (2) The Bootstrap-ISSA-MKELM based on the MTL method has superior performance than that based on the STL method. (3) Through comparing discussion, we discover the established uncertain interval prediction model has the superior performance in combined electricity-heat-cooling-gas loads prediction.
Data Shepherding in Nanotechnology. The Exposure Field Campaign Template

In this paper, we demonstrate the realization process of a pragmatic approach on developing a template for capturing field monitoring data in nanomanufacturing processes. The template serves the fundamental principles which make data scientifically Findable, Accessible, Interoperable and Reusable (FAIR principles), as well as encouraging individuals to reuse it. In our case, the data shepherds' (the guider of data) template creation workflow consists of the following steps: (1) Identify relevant stakeholders, (2) Distribute questionnaires to capture a general description of the data to be generated, (3) Understand the needs and requirements of each stakeholder, (4) Interactive simple communication with the stakeholders for variables/descriptors selection, and (5) Design of the template and annotation of descriptors. We provide an annotated template for capturing exposure field campaign monitoring data, and increase their interoperability, while comparing it with existing templates. This paper enables the data creators of exposure field campaign data to store data in a FAIR way and helps the scientific community, such as data shepherds, by avoiding extensive steps for template creation and by utilizing the pragmatic structure and/or the template proposed herein, in the case of a nanotechnology project (Anticipating Safety Issues at the Design of Nano Product Development, ASINA).
OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Human operators often diagnose industrial machinery via anomalous sounds. Given the new advances in the field of machine learning, automated acoustic anomaly detection can lead to reliable maintenance of machinery. However, deep learning-driven anomaly detection methods often require an extensive amount of computational resources prohibiting their deployment in factories. Here we explore a machine-driven design exploration strategy to create OutlierNets, a family of highly compact deep convolutional autoencoder network architectures featuring as few as 686 parameters, model sizes as small as 2.7 KB, and as low as 2.8 million FLOPs, with a detection accuracy matching or exceeding published architectures with as many as 4 million parameters. The architectures are deployed on an Intel Core i5 as well as a ARM Cortex A72 to assess performance on hardware that is likely to be used in industry. Experimental results on the model’s latency show that the OutlierNet architectures can achieve as much as 30× lower latency than published networks.
Evaluation of the Augmented Reality Educational Application for the 2nd cycle of primary school

The use of augmented reality (AR) technology has been shown to be useful in several areas of human activity. In this sense, this study aims to understand whether these AR technologies can contribute to inform and educate children of the 2nd cycle of primary school about the outbreak by COVID-19. For this purpose, in addition to the systematic review of the literature of the last 5 years on AR, a case study was carried out with a group of experts who evaluated an AR application using Metaverse Studio. The study showed a very favorable response in relation to the use of these applications in the teaching-learning context, namely in basic education, confirming that it can improve the cognitive capacity of children, allowing them to retain knowledge and develop creative and autonomous learning after the experience of using this technology.
An Earthquake Early Warning System for Southwestern British Columbia

Southwestern British Columbia (BC) is exposed to the highest seismic hazard in Canada. Ocean Networks Canada (ONC) has developed an Earthquake Early Warning (EEW) system for the region. The system successfully utilizes offshore cabled seismic instruments in addition to land-based seismic sensors and integrates displacement data from Global Navigation Satellite Systems (GNSS). The seismic and geodetic data are processed in real-time onsite at 40 different stations along the coast of BC. The processing utilizes P-wave and S-wave detection algorithms for epicentre calculations as well as incorporation of geodetic and seismic displacement data into a Kalman filter to provide magnitude estimates. The system is currently in its commissioning phase and has successfully detected over 60 earthquakes since being deployed in October 2018. To increase the coverage of the EEW system, we are in the process of incorporating detection parameters from neighbouring networks (e.g., the Pacific Northwest Seismic Network (PNSN)) to provide additional information for future event notifications.
Distributed Secure Edge Computing Architecture Based on Blockchain for Real-Time Data Integrity in IoT Environments

The traditional cloud-based Internet of Things (IoT) architecture places extremely high demands on computers and storage on cloud servers. At the same time, the strong dependence on centralized servers causes major trust problems. Blockchain provides immutability, transparency, and data encryption based on safety to solve these problems of the IoT. In this paper, we present a distributed secure edge computing architecture using multiple data storages and blockchain agents for the real-time context data integrity in the IoT environment. The proposed distributed secure edge computing architecture provides reliable access and an unlimited repository for scalable and secure transactions. The architecture eliminates traditional centralized servers using an edge computing framework that represents cloud computing for computer and security issues. Also, blockchain-based edge computing-compatible IoT design is supported to achieve the level of security and scalability required for data integrity. Furthermore, we present the blockchain agent to provide internetworking between blockchain networks and edge computing. For experimenting with the proposed architecture in the IoT environment, we implement and perform a concrete IoT environment based on the EdgeX framework and Hyperledger Fabric. The evaluation results are collected by measuring the performance of the edge computing and blockchain platform based on service execution time to verify the proposed architecture in the IoT environment.
Escaping the Gradient Vanishing: Periodic Alternatives of Softmax in Attention Mechanism

Softmax is widely used in neural networks for multiclass classification, gate structure, and attention mechanisms. The statistical assumption that the input is normally distributed supports the gradient stability of softmax. However, when used in attention mechanisms such as transformers, because the correlation scores between embeddings are often not normally distributed, the gradient vanishing problem appears, and we prove this point through experimental confirmation. In this work, we suggest replacing the exponential function with periodic functions, and delve into some potential periodic alternatives of Softmax from the viewpoint of value and gradient. Through experiments on a simply designed demo referenced to LeViT, our method was proven to be able to alleviate the gradient problem and yield substantial improvements compared to Softmax and its variants. Further, we analyze the impact of pre-normalization for Softmax and our methods through mathematics and experiments. Finally, we increase the depth of the demo and prove the applicability of our method to deep structures. The code are available at https://github.com/slwang9353/Period-alternatives-of-Softmax.