Reviewerly/exHarmony huggingface dataset:
Big Data Analysis Research on the Deep Integration of Intangible Cultural Heritage Inheritance and Art Design Education in Colleges and Universities

Intangible cultural heritage involves a large number of national traditions, or folk unique knowledge, language, and customs, derived from folk dance and music as well as traditional medicine and other forms of art. China’s intangible cultural heritage plays a key role in the whole national traditional culture. Strengthening the protection of intangible cultural heritage and promoting its continuous development is an important way and inevitable choice to strengthen cultural self-confidence and build a cultural power. As the builders and successors of the socialist cause, college students must strengthen their cultural self-confidence and shoulder important responsibilities in the construction of a cultural power. Big data is a new technology in the Internet era. Using big data technology to realize the collection, preservation, retrieval, reproduction, integration, dissemination, and utilization of intangible cultural heritage information can better meet the requirements of the times, so as to provide new opportunities, conditions, and means for the inheritance and development of intangible cultural heritage. This article will discuss the use of big data to analyze the research talent training of the deep integration of intangible cultural heritage inheritance and art design education in colleges and universities. This article introduces the background and significance of intangible cultural heritage inheritance and educational integration, introduces its related methods, and then designs a new mode of intangible cultural heritage learning in colleges and universities based on big data. Finally, this article applies the model to college education and then investigates the students’ experience of the new model curriculum, and comes to the conclusion that the proportion of people who choose to like and very much like the new model is 69.3%; 83.3% of the people who chose to agree and strongly agree with the new model were helpful to themselves. In short, the application and design of the new curriculum model is still relatively successful.
A diagnostic prediction model for hypertension in Han and Yugur population from the China National Health Survey (CNHS)

Abstract Background: The prevalence of hypertension is high among Chinese adults, thus, identifying non-hypertensive individuals at high risk for intervention will help to improve the efficiency of primary prevention strategies. Methods: The cross-sectional data on 9699 participants aged 20 to 80 years were collected from the China National Health Survey in Gansu and Hebei provinces in 2016 to 2017, and they were nonrandomly split into the training set and validation set based on location. Multivariable logistic regression analysis was performed to develop the diagnostic prediction model, which was presented as a nomogram and a website with risk classification. Predictive performances of the model were evaluated using discrimination and calibration, and were further compared with a previously published model. Decision curve analysis was used to calculate the standardized net benefit for assessing the clinical usefulness of the model. Results: The Lasso regression analysis identified the significant predictors of hypertension in the training set, and a diagnostic model was developed using logistic regression. A nomogram with risk classification was constructed to visualize the model, and a website (https://chris-yu.shinyapps.io/hypertension_risk_prediction/) was developed to calculate the exact probabilities of hypertension. The model showed good discrimination and calibration, with the C-index of 0.789 (95% confidence interval [CI]: 0.768, 0.810) through internal validation and 0.829 (95% CI: 0.816, 0.842) through external validation. Decision curve analysis demonstrated that the model was clinically useful. The model had a higher area under receiver operating characteristic curves in training and validation sets compared with a previously published diagnostic model based on Northern China population. Conclusion: This study developed and validated a diagnostic model for hypertension prediction in Gansu Province. A nomogram and a website were developed to make the model conveniently used to facilitate the individualized prediction of hypertension in the general population of Han and Yugur.
Advanced security system in video surveillance for COVID-19

Image processing techniques are frequently used to scale, resize, compare, and transform graphical contents. The detection of face mask pattern from an image set could be frequently made using the Convolutional Neural Network (CNN) model. But during CNN based classification, several issues are observed. There has been limited work to improve the performance of face mask detection during COVID–19. However, there is much research in image processing but the time taken for prediction needs to be reduced. Moreover, there is the issue of space consumption by graphical content. Proposed research is supposed to minimize the prediction time and space consumption. Research has focused on the study of existing image processing research and techniques and eliminating their limitations. Research proposes a methodology for face mask detection using an edge–based convolution neural network algorithm. The elimination of useless content from a graphical image before applying Convolutional Neural Network has reduced time consumption. Moreover, it has also reduced the storage requirement for the graphical dataset. As the number of data sets increases, every comparison creates a huge gap in size and comparison time. Proposed work is supposed to implement the proposed methodology using MATLAB. Comparison of the proposed methodology and algorithm with the traditional algorithm is made during simulation. The proposed work is found to be more efficient compared to traditional techniques used in face mask detection. The use of this proposed work in COVID–19 is supposed to improve the capability of convolution neural networking at the time of decision making. The proposed work is supposed to be more accurate compared to the traditional mode. The proposed work would integrate the CNN approach with edge detection mechanisms to improve the performance of the face mask detection mechanism. These systems are connected to a smart video surveillance system to support the notification system in case of absence of a mask. If a mask pattern is not found, then the alarm is triggered to represent the presence of a person without a mask.
Multi-Scale Attention Convolutional Network for Masson Stained Bile Duct Segmentation from Liver Pathology Images

In clinical practice, the Ishak Score system would be adopted to perform the evaluation of the grading and staging of hepatitis according to whether portal areas have fibrous expansion, bridging with other portal areas, or bridging with central veins. Based on these staging criteria, it is necessary to identify portal areas and central veins when performing the Ishak Score staging. The bile ducts have variant types and are very difficult to be detected under a single magnification, hence pathologists must observe bile ducts at different magnifications to obtain sufficient information. This pathologic examinations in routine clinical practice, however, would result in the labor intensive and expensive examination process. Therefore, the automatic quantitative analysis for pathologic examinations has had an increased demand and attracted significant attention recently. A multi-scale inputs of attention convolutional network is proposed in this study to simulate pathologists' examination procedure for observing bile ducts under different magnifications in liver biopsy. The proposed multi-scale attention network integrates cell-level information and adjacent structural feature information for bile duct segmentation. In addition, the attention mechanism of proposed model enables the network to focus the segmentation task on the input of high magnification, reducing the influence from low magnification input, but still helps to provide wider field of surrounding information. In comparison with existing models, including FCN, U-Net, SegNet, DeepLabv3 and DeepLabv3-plus, the experimental results demonstrated that the proposed model improved the segmentation performance on Masson bile duct segmentation task with 72.5% IOU and 84.1% F1-score.
BigDetection: A Large-scale Benchmark for Improved Object Detector Pre-training

Multiple datasets and open challenges for object detection have been introduced in recent years. To build more general and powerful object detection systems, in this paper, we construct a new large-scale benchmark termed BigDetection. Our goal is to simply leverage the training data from existing datasets (LVIS, OpenImages and Object365) with carefully designed principles, and curate a larger dataset for improved detector pre-training. Specifically, we generate a new taxonomy which unifies the heterogeneous label spaces from different sources. Our BigDetection dataset has 600 object categories and contains over 3.4M training images with 36M bounding boxes. It is much larger in multiple dimensions than previous benchmarks, which offers both opportunities and challenges. Extensive experiments demonstrate its validity as a new benchmark for evaluating different object detection methods and its effectiveness as a pre-training dataset. The code and models are available at https://github.com/amazon-research/bigdetection.
GADGET: Online Resource Optimization for Scheduling Ring-All-Reduce Learning Jobs

Fueled by advances in distributed deep learning (DDL), recent years have witnessed a rapidly growing demand for resource-intensive distributed/parallel computing to process DDL computing jobs. To resolve network communication bottleneck and load balancing issues in distributed computing, the so-called "ring-all-reduce" decentralized architecture has been increasingly adopted to remove the need for dedicated parameter servers. To date, however, there remains a lack of theoretical understanding on how to design resource optimization algorithms for efficiently scheduling ring-all-reduce DDL jobs in computing clusters. This motivates us to fill this gap by proposing a series of new resource scheduling designs for ring-all-reduce DDL jobs. Our contributions in this paper are threefold: i) We propose a new resource scheduling analytical model for ring-all-reduce deep learning, which covers a wide range of objectives in DDL performance optimization (e.g., excessive training avoidance, energy efficiency, fairness); ii) Based on the proposed performance analytical model, we develop an efficient resource scheduling algorithm called GADGET (greedy ring-all-reduce distributed graph embedding technique), which enjoys a provable strong performance guarantee; iii) We conduct extensive trace-driven experiments to demonstrate the effectiveness of the GADGET approach and its superiority over the state of the art.
Sentiment Word Aware Multimodal Refinement for Multimodal Sentiment Analysis with ASR Errors

Multimodal sentiment analysis has attracted increasing attention and lots of models have been proposed.However, the performance of the state-of-the-art models decreases sharply when they are deployed in the real world.We find that the main reason is that real-world applications can only access the text outputs by the automatic speech recognition (ASR) models, which may be with errors because of the limitation of model capacity.Through further analysis of the ASR outputs, we find that in some cases the sentiment words, the key sentiment elements in the textual modality, are recognized as other words, which makes the sentiment of the text change and hurts the performance of multimodal sentiment analysis models directly.To address this problem, we propose the sentiment word aware multimodal refinement model (SWRM), which can dynamically refine the erroneous sentiment words by leveraging multimodal sentiment clues.Specifically, we first use the sentiment word position detection module to obtain the most possible position of the sentiment word in the text and then utilize the multimodal sentiment word refinement module to dynamically refine the sentiment word embeddings.The refined embeddings are taken as the textual inputs of the multimodal feature fusion module to predict the sentiment labels.We conduct extensive experiments on the real-world datasets including MOSI-Speechbrain, MOSI-IBM, and MOSI-iFlytek and the results demonstrate the effectiveness of our model, which surpasses the current state-of-the-art models on three datasets.Furthermore, our approach can be adapted for other multimodal feature fusion models easily 1 .
Protecting Vaccine Safety: An Improved, Blockchain-Based, Storage-Efficient Scheme

In recent years, vaccine safety incidents have occurred frequently. To protect vaccine safety, researchers have proposed to use blockchain to secure the vaccine circulation process. Technically, blockchain has some limitations in solving vaccine and other supply chain problems, such as large on-chain storage consumption and low throughput. To better alleviate these restrictions, we propose an improved, blockchain-based, storage-efficient vaccine safety protection scheme in this work. Specifically, we first model the vaccine circulation process. We then design a system to protect vaccine circulation using blockchain, cloud, and cryptographic mechanisms. The proposed system leverages the cloud to implement the vaccine circulation model. Correspondingly, it uses the blockchain to store circulating data certificates and signatures. We evaluated the proposed conceptual model using a consortium blockchain. The experimental results show that the proposed system is efficient.
Sentiment Analysis of Cyber Security Content on Twitter and Reddit

Sentiment Analysis provides an opportunity to understand the subject(s), especially in the digital age, due to an abundance of public data and effective algorithms. Cybersecurity is a subject where opinions are plentiful and differing in the public domain. This descriptive research analyzed cybersecurity content on Twitter and Reddit to measure its sentiment, positive or negative, or neutral. The data from Twitter and Reddit was amassed via technology-specific APIs during a selected timeframe to create datasets, which were then analyzed individually for their sentiment by VADER, an NLP (Natural Language Processing) algorithm. A random sample of cybersecurity content (ten tweets and posts) was also classified for sentiments by twenty human annotators to evaluate the performance of VADER. Cybersecurity content on Twitter was at least 48% positive, and Reddit was at least 26.5% positive. The positive or neutral content far outweighed negative sentiments across both platforms. When compared to human classification, which was considered the standard or source of truth, VADER produced 60% accuracy for Twitter and 70% for Reddit in assessing the sentiment; in other words, some agreement between algorithm and human classifiers. Overall, the goal was to explore an uninhibited research topic about cybersecurity sentiment.
In-cAR Gaming: Exploring the use of AR headsets to Leverage Passenger Travel Environments for Mixed Reality Gameplay

Autonomous cars offer passengers a rich platform for Augmented Reality entertainment, with complex sensing that can drive passenger experiences by tracking, appropriating and altering elements of reality. This paper forms an early exploration of in-car AR games, starting with how existing game genres might work within an AR vehicle context, appropriating elements of reality (e.g. other cars) into gameplay, and altering the appearance of reality in relation to game events (e.g. augmented cracks in car windows). We discuss results from focus groups exploring an initial AR game prototype, and an informal in-car evaluation of a follow-up prototype inspired by the focus groups. Broadly, we found that participants enjoyed using AR gaming in-car, noted the immersive impact of appropriating real-world elements into gameplay, and felt that it improved their experience of the journey. We reflect on the ways in which future AR passenger experiences might take advantage of the available sensing and environment to create engaging reality-based gameplay.
VisImages: A Fine-Grained Expert-Annotated Visualization Dataset

Images in visualization publications contain rich information, e.g., novel visualization designs and implicit design patterns of visualizations. A systematic collection of these images can contribute to the community in many aspects, such as literature analysis and automated tasks for visualization. In this paper, we build and make public a dataset, VisImages, which collects 12,267 images with captions from 1,397 papers in IEEE InfoVis and VAST. Built upon a comprehensive visualization taxonomy, the dataset includes 35,096 visualizations and their bounding boxes in the images. We demonstrate the usefulness of VisImages through three use cases: 1) investigating the use of visualizations in the publications with VisImages Explorer, 2) training and benchmarking models for visualization classification, and 3) localizing visualizations in the visual analytics systems automatically.
A Flexible Smoother Adapted to Censored Data With Outliers and Its Application to SARS-CoV-2 Monitoring in Wastewater

A sentinel network, Obépine , has been designed to monitor SARS-CoV-2 viral load in wastewaters arriving at wastewater treatment plants (WWTPs) in France as an indirect macro-epidemiological parameter. The sources of uncertainty in such a monitoring system are numerous, and the concentration measurements it provides are left-censored and contain outliers, which biases the results of usual smoothing methods. Hence, the need for an adapted pre-processing in order to evaluate the real daily amount of viruses arriving at each WWTP. We propose a method based on an auto-regressive model adapted to censored data with outliers. Inference and prediction are produced via a discretized smoother which makes it a very flexible tool. This method is both validated on simulations and real data from Obépine . The resulting smoothed signal shows a good correlation with other epidemiological indicators and is currently used by Obépine to provide an estimate of virus circulation over the watersheds corresponding to about 200 WWTPs.
Interactive Visual Pattern Search on Graph Data via Graph Representation Learning

Graphs are a ubiquitous data structure to model processes and relations in a wide range of domains. Examples include control-flow graphs in programs and semantic scene graphs in images. Identifying subgraph patterns in graphs is an important approach to understand their structural properties. We propose a visual analytics system GraphQ to support human-in-the-loop, example-based, subgraph pattern search in a database containing many individual graphs. To support fast, interactive queries, we use graph neural networks (GNNs) to encode a graph as fixed-length latent vector representation, and perform subgraph matching in the latent space. Due to the complexity of the problem, it is still difficult to obtain accurate one-to-one node correspondences in the matching results that are crucial for visualization and interpretation. We, therefore, propose a novel GNN for node-alignment called NeuroAlign, to facilitate easy validation and interpretation of the query results. GraphQ provides a visual query interface with a query editor and a multi-scale visualization of the results, as well as a user feedback mechanism for refining the results with additional constraints. We demonstrate GraphQ through two example usage scenarios: analyzing reusable subroutines in program workflows and semantic scene graph search in images. Quantitative experiments show that NeuroAlign achieves 19%-29% improvement in node-alignment accuracy compared to baseline GNN and provides up to 100× speedup compared to combinatorial algorithms. Our qualitative study with domain experts confirms the effectiveness for both usage scenarios.
Emission trading innovation mechanism based on blockchain

The rationality and scientific nature of the emission trading mechanism is the key to the effective implementation of environmental and economic policies. As far as China is concerned, there are phenomena such as information asymmetry, low supervision efficiency, and alienation of government and enterprise behaviors caused by the incomplete mechanism of emission trading in the practice of different pilots. The introduction of blockchain technology can innovate the traditional transaction model and form a decentralized peer-to-peer transaction and a trusted emission trading market. To this end, based on the current emission trading mechanism and the characteristics of blockchain technology, this paper couples the core technologies of blockchain with the functional requirements of application scenarios. Then, an innovative application framework is built based on the consortium blockchain Fabric from three aspects: emission trading supervision, secondary trading market construction, as well as emission trading incentive and punishment mechanisms. Technologies such as the consensus mechanism, smart contract, Merkle tree and asymmetric encryption are comprehensively applied in this process. In the construction of the blockchain framework of the secondary market for emission trading, institutional changes and innovations brought about by the blockchain at various levels are analyzed in terms of participants, transaction processes and the transaction scope. At the same time, smart contract functions and algorithms are designed for the purchase, transfer-out and trading of emission rights, and the operation business logic of the smart contract is analyzed. On the whole, this paper explores the application framework of blockchain technology in the field of emission trading at the macro level, and analyzes the application mechanism of the corresponding technologies of blockchain at each coupling point in the framework at the micro level. The collaborative analysis at the two levels shows that blockchain technology and the requirements of emission trading mechanism can be effectively coupled, and the application of blockchain technology can promote the effective supervision of enterprises' emission behavior, making the processes of the purchase, transfer and transaction of emission rights intelligent and automated, and providing technical support for cross-regional emission trading to reduce transaction costs and management complexity. In addition, the issuance of emission credits based on smart contract will be a new incentive for companies to actively participate in transactions. Based on the above analysis, this paper believes that the innovative application of blockchain technology is of great significance in the promotion of the market-based allocation of element of emission trading and the rational allocation of environmental resources. It will lead to a major breakthrough in the traditional trading system in terms of trading modes, forming a value transmission network of environmental resources between the government and polluters.