Reviewerly/exHarmony huggingface dataset:
A Review on Twitter Data Sentiment Analysis Related to COVID-19

On March 11, 2020, Dr. Tedros Adhanom Ghebreyesus, Director-General of the WHO, pronounced the outbreak a pandemic. The term “pandemic” refers to a disease that spreads rapidly and engulfs an entire geographic region. Coronavirus is a brand-new viral disease named after the year it first appeared. There is a scarcity of academic research on the subject to help researchers. Social media content analysis can reveal a lot concerning the general temperament and mood of the human race. In the field of sentiment analysis, deep learning models have been widely used. Sentiment analysis is a set of techniques, tools, and methods for detecting and extracting information. People have been using social networking sites like Twitter to voice their opinions, report realities, and provide a point of view on what is happening in the world today. Folks have always used Twitter to share data about the COVID-19 pandemic. People randomly share data visualizations from news revealed by organizations and the government. The numerous studies surveyed are selected based on a similarity. Every paper which is supervised performs sentiment analysis of Twitter data. Various studies have made used a fusion of diverse word embedding’s with either machine learning classifiers or deep learning classifiers. Albeit the interpretation of single classifiers is satisfactory, the studies those proposed hybrid models have shown outstanding performance. On top of that transformer based models demonstrated quality results. It is concluded that using hybrid classifiers on Twitter data for sentiment analysis can surpass the achievements of the single classifiers.
Introduction To Machine Learning

Machine Learning is referred to as the subset of Artificial Intelligence. It involves a machine being learned without programming it explicitly. In Machine Learning, machines try to improve their performance with the help of past experiences or by using training examples. The following chapter discusses Machine Learning, Selection of training set, Selection of target function, Selection of Function Approximation Algorithm, Perspectives and issues involved while building a machine learning system,In-sample and Out-of-sample error and Applications of Machine Learning.&lt;br&gt;
Cross-Departmental Collaboration Approach for Earthquake Emergency Response Based on Synchronous Intersection between Traditional and Logical Petri Nets

In order to reduce the harm of earthquakes to human society, all governments actively promote the construction and development of earthquake emergency rescue work. The earthquake emergency response involves many departments, multiple personnel and large rescue forces, which presents a great challenge to the ability to carry out cross-departmental rescue work in a collaborative and joint manner. A novel collaboration approach based on traditional and logical Petri nets is proposed to improve the cross-departmental collaboration in earthquake emergency response. The approach extends the synchronization of transitions in traditional Petri nets to that of traditional and logical transitions in traditional and logical Petri nets, and defines the intersection of related logical functions. The approach builds a model for the earthquake emergency response plans of various departments with the help of traditional and logical Petri net models, and then performs synchronous intersection operations on the two kinds of Petri nets and merges the two kinds of Petri nets into new logical Petri nets. Meanwhile, through realizing the collaboration ability of cross-departmental work, the approach improves the rescue efficiency and reduces the damage of the earthquake emergency.
A 3D-CLDNN Based Multiple Data Fusion Framework for Finger Gesture Recognition in Human-Robot Interaction

Finger gesture recognition using surface electromyography (sEMG) became an efficient Human-Robot Interaction (HRI) solution. Although Machine Learning (ML) techniques are widely applied in this field, the general solutions for labeling and collecting big datasets impose time-consuming implementation and heavy workloads. In this paper, a new deep learning structure, namely three-dimensional convolutional long short-term memory neural networks (3D-CLDNN) for finger gesture identification based on depth vision and sEMG signals, was proposed for human-machine interaction. It automatically labels the depth data by the self-organizing map (SOM) and predicts the hand gesture only adopting sEMG signals. The 3D-CLDNN method is integrated to improve the recognition rate and computational speed. The results showed the highest clustering accuracy (98.60%) and highest accuracy (84.40%) with the lowest computational time compared with different approaches. Finally, real-time human-machine interaction experiments are performed to demonstrate its efficiency.
Challenges, Issues, and Recommendations for Blockchain- and Cloud-Based Automotive Insurance Systems

Despite the rapid expansion in the insurance industry, many issues remain unresolved and may require immediate action. As the insurance sector continues to evolve with the development of new technologies, it faces more challenges, especially related to data security and fraud. The fraud-prevention data and tactics presently used by insurance firms are outdated and ineffective. Additionally, insurance firms have traditionally handled the settlement of all consumer claims through lengthy manual processes. These manual processes need to be changed to provide opportunities for insurance businesses to grow. In the case of vehicles, the information obtained from an automobile data recorder can be used as evidence. Data from automated vehicles are critical because they can help the police, law enforcement agencies, and insurance companies to reconstruct the events leading up to a collision. Insurance companies require the forensic analysis of accident videos, which is a time-consuming process and involves a large amount of storage. Due to hardware limitations and associated costs, the current standalone (and often dedicated) computing infrastructures used for this purpose are quite limited. Previous research focused on simple video analysis tasks within cloud computing and blockchain technology. The requirements for a large-scale auto-insurance system are quite high and need more thorough investigation. In this paper, a review of the contribution of recent approaches to storing accidental data in cloud computing using blockchain is provided. We focused on the latest cloud and blockchain studies related to auto-insurance along with the related issues and challenges. Some useful solutions and recommendations are provided to address the identified issues and challenges in the cloud-based and blockchain-based auto-insurance sector.
Pass-Efficient Randomized SVD with Boosted Accuracy

Singular value decomposition (SVD) is a widely used tool in data analysis and numerical linear algebra. Computing truncated SVD of a very large matrix encounters difficulty due to excessive time and memory cost. In this work, we aim to tackle this difficulty and enable accurate SVD computation for the large data which cannot be loaded into memory. We first propose a randomized SVD algorithm with fewer passes over the matrix. It reduces the passes in the basic randomized SVD by half, almost not sacrificing accuracy. Then, a shifted power iteration technique is proposed to improve the accuracy of result, where a dynamic scheme of updating the shift value in each power iteration is included. Finally, collaborating the proposed techniques with several accelerating skills, we develop a Pass-efficient randomized SVD (PerSVD) algorithm for efficient and accurate treatment of large data stored on hard disk. Experiments on synthetic and real-world data validate that the proposed techniques largely improve the accuracy of randomized SVD with same number of passes over the matrix. With 3 or 4 passes over the data, PerSVD is able to reduce the error of SVD result by three or four orders of magnitude compared with the basic randomized SVD and single-pass SVD algorithms, with similar or less runtime and less memory usage.
Intra- and Inter-Class Induced Discriminative Deep Dictionary Learning for Visual Recognition

Deep dictionary learning (DDL) aims to learn dictionaries at different levels and the deepest level representations. However, existing DDL algorithms impose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{1}$</tex-math></inline-formula> -norm constraint on the deepest level representations, ignoring the constraints on different level representations. Meanwhile, they fail to discover effectively the essential discrimination information. Therefore, the obtained representations are less discriminative, which degrades model performance. To tackle those issues, we propose an intra- and inter-class induced discriminative deep dictionary learning (DDDL). Specifically, both intra-class compactness and inter-class separability of layer-wise data representations are newly devised as two discriminative constraints on deep dictionary learning. In a hierarchical structure, we obtain a more informative dictionary and the class-specific representations are thus more discriminative at each layer. Due to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$l_{2}$</tex-math></inline-formula> -norm intra- and inter-class constraints of layer-wise data representation, we devise a layer-wise optimization strategy to efficiently learn the closed-form solution of the deepest representation for classification. Comprehensive experiments and analyses on several visual recognition tasks show that our DDDL model surpasses recent shallow and deep representation learning approaches.
[Enrichment Characteristics, Source Apportionment, and Risk Assessment of Heavy Metals in the Industrial and Mining Area of Northern Guangdong Province].

In order to understand the heavy metal pollution of the industrial and mining area in northern Guangdong Province, topsoil samples (0-20 cm) from 209 sites in study area were collected, and the concentrations of As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were analyzed. The source and distribution characteristics of Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn in soil samples were analyzed using the geographic information system (GIS) and principal component analysis (PCA), and the geo-accumulation index and potential ecological hazard index were used to evaluate their pollution status and ecological risk. The results showed that:① Except Ni, all seven heavy metal elements exceeded the national soil background value but were below the pollution risk screening value. The soil environment as a whole was relatively clean. ② The spatial distribution of heavy metals in soil differed. The contents of As and Pb showed northwest-southeast zonal distribution. The contents of Cd, Cu, Hg, and Zn generally decreased from the middle to the surrounding, which was consistent with the industrial and mining enterprise locations. However, the spatial distribution of Cr and Ni had no direct relationship with the location of pollution sources such as industrial enterprises. ③ The eight heavy metals could be identified as three principal components (PCs). PC1 (Cd, Cu, Pb, and Zn) was mainly affected by human activities such as lead-zinc deposit dressing, traffic emissions, and agricultural production. However, PC2 (Cr and Ni) was a natural source, mainly affected by soil parent material. In addition, PC3 (As and Hg) was mainly affected by industrial activities such as non-ferrous metal smelting and thermal power generation. ④ According to the geo-accumulation index method, the risk degree of the eight heavy metals was:Cd>As>Zn>Hg>Pb>Cu>Cr=Ni. The contents of Cr and Ni in soil were at a no-risk level; the contents of As, Cu, Hg, Pb, and Zn were at a low-risk level; and the content of Cd was at a high-risk level. Most potential ecological hazards for single elements in the study area were at a mild risk level. In contrast, a small proportion of the surface soils in areas of intense industrial activity were subject to substantial levels of heavy metal stress and require further attention.
Geometric prior guided hybrid deep neural network for facial beauty analysis

Abstract Facial beauty analysis is an important topic in human society. It may be used as a guidance for face beautification applications such as cosmetic surgery. Deep neural networks (DNNs) have recently been adopted for facial beauty analysis and have achieved remarkable performance. However, most existing DNN‐based models regard facial beauty analysis as a normal classification task. They ignore important prior knowledge in traditional machine learning models which illustrate the significant contribution of the geometric features in facial beauty analysis. To be specific, landmarks of the whole face and facial organs are introduced to extract geometric features to make the decision. Inspired by this, we introduce a novel dual‐branch network for facial beauty analysis: one branch takes the Swin Transformer as the backbone to model the full face and global patterns, and another branch focuses on the masked facial organs with the residual network to model the local patterns of certain facial parts. Additionally, the designed multi‐scale feature fusion module can further facilitate our network to learn complementary semantic information between the two branches. In model optimisation, we propose a hybrid loss function, where especially geometric regulation is introduced by regressing the facial landmarks and it can force the extracted features to convey facial geometric features. Experiments performed on the SCUT‐FBP5500 dataset and the SCUT‐FBP dataset demonstrate that our model outperforms the state‐of‐the‐art convolutional neural networks models, which proves the effectiveness of the proposed geometric regularisation and dual‐branch structure with the hybrid network. To the best of our knowledge, this is the first study to introduce a Vision Transformer into the facial beauty analysis task.
Disaster-resilient villages: strengthening community capacity in flood disasters managing in wetland areas

Wetland areas have the potential for flooding during the rainy season. Even though the community is used to a flooded environment, disaster risk reduction must be carried out immediately when the inundation exceeds the standard limit and causes losses including establishing a disaster-resilient village. This study aims to analyze the role of disaster-resilient villages in strengthening communities in the face of flood disasters in wetland areas. This study used a questionnaire with 100 respondents from two flood-prone villages, Sungai Lumbah Village and Bangkit Baru Village. In addition, in-depth interviews were conducted with community leaders in the two villages, the Sungai Lumbah Village Head, Bangkit Baru Village Head, volunteer groups and the Head of the Barito Kuala District Disaster Management Agency. The results showed that the two villages were still included in the Primary Disaster Resilient Village category. The two villages were still not ready when the disaster struck. The institutional aspect is still not prepared, the funding aspect is still low and the planning aspect is inferior. The communities of the two villages were still not ready when the disaster occurred. Various efforts were made to increase socialization from both the Government and academia to increase the number of volunteers and to increase training to create a disaster-resilient society.
YOLO Series for Human Hand Action Detection and Classification from Egocentric Videos

Hand detection and classification is a very important pre-processing step in building applications based on three-dimensional (3D) hand pose estimation and hand activity recognition. To automatically limit the hand data area on egocentric vision (EV) datasets, especially to see the development and performance of the “You Only Live Once” (YOLO) network over the past seven years, we propose a study comparing the efficiency of hand detection and classification based on the YOLO-family networks. This study is based on the following problems: (1) systematizing all architectures, advantages, and disadvantages of YOLO-family networks from version (v)1 to v7; (2) preparing ground-truth data for pre-trained models and evaluation models of hand detection and classification on EV datasets (FPHAB, HOI4D, RehabHand); (3) fine-tuning the hand detection and classification model based on the YOLO-family networks, hand detection, and classification evaluation on the EV datasets. Hand detection and classification results on the YOLOv7 network and its variations were the best across all three datasets. The results of the YOLOv7-w6 network are as follows: FPHAB is P = 97% with TheshIOU = 0.5; HOI4D is P = 95% with TheshIOU = 0.5; RehabHand is larger than 95% with TheshIOU = 0.5; the processing speed of YOLOv7-w6 is 60 fps with a resolution of 1280 × 1280 pixels and that of YOLOv7 is 133 fps with a resolution of 640 × 640 pixels.
Convolutional Learning on Multigraphs

Graph convolutional learning has led to many exciting discoveries in diverse areas. However, in some applications, traditional graphs are insufficient to capture the structure and intricacies of the data. In such scenarios, multigraphs arise naturally as discrete structures in which complex dynamics can be embedded. In this paper, we develop convolutional information processing on multigraphs and introduce convolutional multigraph neural networks (MGNNs). To capture the complex dynamics of information diffusion within and across each of the multigraph's classes of edges, we formalize a convolutional signal processing model, defining the notions of signals, filtering, and frequency representations on multigraphs. Leveraging this model, we develop a multigraph learning architecture, including a generalization of selection sampling to reduce computational complexity. The introduced architecture is applied towards optimal wireless resource allocation and a hate speech localization task, offering improved performance over traditional graph neural networks.
Cloud detection method based on clear sky background under multiple weather conditions

This paper deals with an image processing methodology based on a clear sky library for cloud detection. It is a part of a project which aims at using the detection results of ground-based cloud images in combination with solar irradiation data to complete power forecasts for solar power plants at ultra-short-term horizons (15 min). This paper focuses on the implementation of the cloud detection process. To overcome the problem of detection failure caused by uneven background brightness distribution in the sky, detection work is achieved by building a library of clear sky background images for various weather conditions at different solar zenith angles and combining background elimination methods through image rotation and image matching algorithms. A hybrid algorithm is proposed for detection failure when the sun is blocked by the cloud to achieve secondary processing. The test results show that this method has good detection accuracy at the visual level compared to traditional algorithms, especially for situations with low cloud and sky discrepancies such as thin clouds and hazy weather.
Learning metric volume estimation of fruits and vegetables from short monocular video sequences

We present a novel approach for extracting metric volume information of fruits and vegetables from short monocular video sequences and associated inertial data recorded with a hand-held smartphone. Estimated segmentation masks from a pre-trained object detector are fused with the predicted change in relative pose obtained from the inertial data to predict the class and volume of the objects of interest. Our approach works with simple RGB video frames and inertial data which are readily available from modern smartphones. It does not require reference objects of known size in the video frames. Using a balanced validation dataset, we achieve a classification accuracy of 95% and a mean absolute percentage error for the volume prediction of 16% on untrained objects, which is comparable to state-of-the-art results requiring more elaborated data recording setups. A very accurate estimation of the model uncertainty is achieved through ensembling and the use of Gaussian negative log-likelihood loss. The dataset used in our experiments including ground-truth volume information is available at https://sst.aau.at/cns/datasets.
Introducing an Interpretable Deep Learning Approach to Domain-Specific Dictionary Creation: A Use Case for Conflict Prediction

Abstract Recent advancements in natural language processing (NLP) methods have significantly improved their performance. However, more complex NLP models are more difficult to interpret and computationally expensive. Therefore, we propose an approach to dictionary creation that carefully balances the trade-off between complexity and interpretability. This approach combines a deep neural network architecture with techniques to improve model explainability to automatically build a domain-specific dictionary. As an illustrative use case of our approach, we create an objective dictionary that can infer conflict intensity from text data. We train the neural networks on a corpus of conflict reports and match them with conflict event data. This corpus consists of over 14,000 expert-written International Crisis Group (ICG) CrisisWatch reports between 2003 and 2021. Sensitivity analysis is used to extract the weighted words from the neural network to build the dictionary. In order to evaluate our approach, we compare our results to state-of-the-art deep learning language models, text-scaling methods, as well as standard, nonspecialized, and conflict event dictionary approaches. We are able to show that our approach outperforms other approaches while retaining interpretability.
Principal component analysis for accelerating color bilateral filtering

In this paper, we propose to speed up bilateral filtering by principal component analysis (PCA)-based dimensionality compression method with constant-time bilateral filtering. Constant-time bilateral filtering speeds up the filtering by representing it as a summation of the multiple Gaussian filters. However, a simple implementation is of the order of O(K<sup>3</sup>) for color and suffers from the curse of dimensionality. A clustering-based approximation speedup solves this problem with an order of O(K) or O(K<sup>2</sup>). PCA can provide a more informative signal relative to the transformation matrix. We have accelerated the process by converting the color information of the input image from 3-channel to 1-channel by PCA, considering the constant-time bilateral filter as joint bilateral filtering, and using the transformed image as a guide image. This dimensional reduction allowed us to filter images with sufficient accuracy at a higher speed.