Reviewerly/exHarmony huggingface dataset:
Extracting Spatio-Temporal Information from Chinese Archaeological Site Text

Archaeological site text is the main carrier of archaeological data at present, which contains rich information. How to efficiently extract useful knowledge from the massive unstructured archaeological site texts is of great significance for the mining and reuse of archaeological information. According to the site information (such as name, location, cultural type, dynasty, etc.) recorded in the Chinese archaeological site text, this paper combines deep learning and natural language processing techniques to study the information extraction method for automatically obtaining the spatio-temporal information of sites. The initial construction of the corpus of Chinese archaeological site text is completed for the first time, and the corpus is input into the Bidirectional Long Short-Term Memory with Conditional Random Fields (BiLSTM-CRF) entity recognition model and Bidirectional Gated Recurrent Units with Dual Attention (BiGRU-Dual Attention) relationship extraction model for training. The F1 values of BiLSTM-CRF model and BiGRU-Dual Attention model on the test set reach 87.87% and 88.05%, respectively. The study demonstrates that the information extraction method proposed in this paper is feasible for the Chinese archaeological site texts, which promotes the establishment of knowledge graphs in archaeology and provides new methods and ideas for the development of information mining technology in archaeology.
An overview of the most influential journals in fuzzy systems research

Fuzzy systems research (FSR) is a continuously growing academic field which developments and applications extend to a wide-ranging variety of scientific areas. Academic journals play a decisive role in the scientific communication process and the dissemination of novel ideas. The current developments in information technologies bring both challenges and opportunities for scientific journals to analyze, debate, and share scientific findings. Applying bibliometric and network visualization techniques, this paper seeks to analyze the evolution of the FSR field based on journals from 1965 to 2019. The aim is to visualize the current landscape of key highly oriented FSR journals, focusing on productivity, influence, and synergy. The paper uses data retrieved from the Web of Science scientific database and uses a combined methodology specifically designed to maximize the retrieval of significant documents. Results show that the journals with the highest productivity analyzed by period are Mathematical Analysis and Applications (1965–1979), Fuzzy Sets and Systems [FSS](1980–2009), and the Journal of Intelligent Fuzzy Systems (2010–2019). The most cited journals of the last 20 years are FSS, IEEE Transactions on Fuzzy Systems, and IEEE Transactions on Systems Man Cybernetics Systems, while the most relevant for impact factor is Information Fusion. Moreover, a bibliographic coupling analysis by decades is proposed, showing not only the natural impact and relation that the interaction between journals generate but the impact that the rapid development in communications has in the diversification and dissemination of influence, citations, and productivity of scientific journals.
Multilabel Classification Methods for Human Activity Recognition: A Comparison of Algorithms

As the world's population is aging, and since access to ambient sensors has become easier over the past years, activity recognition in smart home installations has gained increased scientific interest. The majority of published papers in the literature focus on single-resident activity recognition. While this is an important area, especially when focusing on elderly people living alone, multi-resident activity recognition has potentially more applications in smart homes. Activity recognition for multiple residents acting concurrently can be treated as a multilabel classification problem (MLC). In this study, an experimental comparison between different MLC algorithms is attempted. Three different techniques were implemented: RAkELd, classifier chains, and binary relevance. These methods are evaluated using the ARAS and CASAS public datasets. Results obtained from experiments have shown that using MLC can recognize activities performed by multiple people with high accuracy. While RAkELd had the best performance, the rest of the methods had on-par results.
The Soil Quality Analysis using K-Mean Technique and Model Color

The objectives of paper are to design and develop procedures for soil quality analysis using image processing techniques and this research proposes soil quality analysis processes using K-mean grouping techniques and color modeling in soil quality analysis to help solve problems in soil quality analysis. Manage soil quality to be suitable for cultivation. by bringing soil data from the Department of Land Development and experts in soil management to assist in the development of advice, including transfer of knowledge in the development of information systems for soil analysis for the benefit of improving the quality of the soil in agriculture and the stability of the agricultural occupation of Thailand in the future, this research has 75% experimental results.
Feature-guided Multimodal Sentiment Analysis towards Industry 4.0

Combining Artificial Intelligence (AI) to process rich media information has become an important part of Industry 4.0. Sentiment recognition in AI aims to analyze user emotions contained in rich media to facilitate service enhancement. Previous research on sentiment recognition has mainly focused on academia, and few have discussed algorithmic applications and innovations in industry. In this paper, we propose a general approach for multimodal sentiment recognition for images and text. The method provides a new approach for processing rich media information by fully considering the internal features of each modality itself as well as the correlations between the modalities. In the dataset constructed in this paper, the accuracy rate is improved by more than 4% compared with the method using single modality. The effectiveness and generality of the method in multimodal sentiment recognition is demonstrated by extending the experiments with a multimodal public dataset.
Techniques and countermeasures for preventing insider threats

With the wide use of technologies nowadays, various security issues have emerged. Public and private sectors are both spending a large portion of their budget to protect the confidentiality, integrity, and availability of their data from possible attacks. Among these attacks are insider attacks which are more serious than external attacks, as insiders are authorized users who have legitimate access to sensitive assets of an organization. As a result, several studies exist in the literature aimed to develop techniques and tools to detect and prevent various types of insider threats. This article reviews different techniques and countermeasures that are proposed to prevent insider attacks. A unified classification model is proposed to classify the insider threat prevention approaches into two categories (biometric-based and asset-based metric). The biometric-based category is also classified into (physiological, behavioral and physical), while the asset metric-based category is also classified into (host, network and combined). This classification systematizes the reviewed approaches that are validated with empirical results utilizing the grounded theory method for rigorous literature review. Additionally, the article compares and discusses significant theoretical and empirical factors that play a key role in the effectiveness of insider threat prevention approaches (e.g., datasets, feature domains, classification algorithms, evaluation metrics, real-world simulation, stability and scalability, etc.). Major challenges are also highlighted which need to be considered when deploying real-world insider threat prevention systems. Some research gaps and recommendations are also presented for future research directions.
A zeroing neural dynamics based acceleration optimization approach for optimizers in deep neural networks

The first-order optimizers in deep neural networks (DNN) are of pivotal essence for a concrete loss function to reach the local minimum or global one on the loss surface within convergence time. However, each optimizer possesses its own superiority and virtue when encountering a specific application scene and environment. In addition, the existing modified optimizers mostly emphasize a given optimizer without any transfer property. In this paper, a zeroing neural dynamics (ZND) based optimization approach for the first-order optimizers is proposed, which can assist ZND via the activation function to expedite the process of solving gradient information, with lower loss and higher accuracy. To the best of our knowledge, it is the first work to integrate the ZND in control domain with the first-order optimizers in DNN. This generic work is an optimization method for the most commonly-used first-order optimizers to handle different application scenes, rather than developing a brand-new algorithm besides the existing optimizers or their modifications. Furthermore, mathematic derivations concerning the gradient information transformation of the ZND are systematically provided. Finally, comparison experiments are implemented, which demonstrates the effectiveness of the proposed approach with different loss functions and network frameworks on the Reuters, CIFAR, and MNIST data sets.
Multi Class Classification of Acute Leukemia using Transfer Learning

Leukemia is a fatal disease which affects blood cells. Detection of this disease using machine learning and deep learning techniques can reduce the burden of doctors and clinicians. The proposed work performs a multi level classification of acute leukemia involving transfer learning technique. An accuracy of 95&#x0025; and 96&#x0025; are achieved for support vector machine and logistic regression, respectively, when features extracted by ResNet50 are used for training. 95&#x0025; test accuracy is obtained for k-Nearest Neighbor classifier (K&#x003D;5) when features extracted by MobileNet are used for training.
Learning dynamic background for weakly supervised moving object detection

Moving Object Detection (MOD) aims at extracting foreground moving objects in videos from static cameras. While low-rank based approaches have achieved impressive success in the MOD task, their performance remains limited on dynamics background scenes. The main reason is that dynamic clutters, e.g., swaying leaves and rippers, are easy to mix up with moving objects in the decomposition model which simply classify the sparse noise as foregrounds. In order to improve the generalization ability of low-rank based moving object detectors, we suggest adding an explicit dynamic clutter component in the decomposition framework with realistic dynamic background modeling. Then the dynamic clutter can be learned through object-free video data due to their self-similarity across time and space. Thus, the moving objects can be naturally separated by a tensor-based decomposition model which formulates the static background by a unidirectional low-rank tensor, learns the dynamic clutter by a two-stream neural network, and constrains moving objects with spatiotemporal continuity. To further provide a more accurate object detection result, an objectness prior is embedded into our model in an attention manner. Extensive experimental results on the challenging datasets of dynamic background clearly demonstrate the superior performance of our model over the state-of-the-art in terms of quantitative metrics and visual quality.
Fast label-free recognition of NRBCs by deep-learning visual object detection and single-cell Raman spectroscopy

Nucleated red blood cells (NRBCs) as a type of rare cell present in an adult's peripheral blood is a concern in hematology, intensive care medicine and prenatal diagnostics. However, it is labor-intensive to screen such rare cells from real complex cell mixtures especially in a label-free way. Herein, we report a new label-free method that incorporates image recognition and Raman spectroscopy for fast recognition of the rare cells in blood. First, we identified unlabeled NRBCs based on both Raman signals of hemoglobin and nucleated morphology, and recorded their microscopic image characteristics which were different enough from other blood cells in unlabeled morphology. Then, two deep-learning algorithms of visual object detection, Faster RCNN and YOLOv3, were investigated for cell morphological recognition on a low-cost computer configuration, and YOLOv3 was demonstrated to be more competent for real-time detection despite slightly lower precision. Finally, several NRBCs were successfully found in maternal blood using this method, which verified the methodological feasibility. Thus, we believe such a labor-saving approach might inspire a new idea for detecting rare cells from complex cell mixtures in a label-free and computer-assisted way.
3D shape measurement based on the unequal-period combination of shifting Gray code and dual-frequency phase-shifting fringes

The combination of Gray code and phase-shifting fringes is widely used in the 3D structured light measurement. However, an error in the period order of the phase-shifting fringe obtained by the Gray code results in a period jump error approximately equal to an integer multiple of 2π. This error causes a gross measurement error, and even a measurement failure for a measured surface whose height changes drastically. In order to effectively reduce this error, a 3D measurement method, based on the unequal-period combination of shifting Gray code and dual-frequency phase-shifting fringes, is proposed by analyzing the generation mechanism of the period jump error. In the proposed method, on the basis of the unequal-period combination of shifting Gray code and single-frequency phase-shifting fringe, a strategy of additionally projecting a set of low-frequency phase-shifting fringe patterns is adopted to effectively correct the period jump error. A measurement model of this method is established, its applicable conditions are given, the experimental equipment is built, and verification experiments are carried out. The experimental results show that the proposed method can effectively reduce the period jump error, and can measure multiple isolated objects and objects with drastic changes in surface height such as plaster heads.
Facemask Detection to Prevent COVID-19 Using YOLOv4 Deep Learning Model

The on-going global Covid-19 pandemic has impacted everyone’s life. World Health organization (WHO) and Governments all over world have found that social distancing and donning a mask in public places has been instrumental in reducing the rate of COVID-19 transmission. Stepping out of homes in a face mask is a social obligation and a law mandate that is often violated by people and hence a face mask detection model that is accessible and efficient will aid in curbing the spread of disease. Detecting and identifying a face mask on an individual in real time can be a daunting and challenging task but using deep learning and computer vision, establish tech-based solutions that can help combat COVID-19 pandemic. In this paper, YOLOv4 deep learning model is designed and applied deep transfer learning approach to create a face mask detector which can be used in real time. GPU used was Google Collab to run the simulations and to draw inferences. Proposed implementation considered three types of data as input such as image dataset, video dataset and real time data for face mask detection. Performance parameters are tabulated and obtained mean average precision of 0.86, F1 score 0.77 for image dataset, 90 % accuracy for video dataset. And real time face mask detector with accuracy of 95%, it is successfully able to identify a person with and without facemask and report if they are wearing a face mask or not.
Soil-sediment linkage and trace element contamination in forested/deforested areas of the Itacaiúnas River Watershed, Brazil: To what extent land-use change plays a role?

Trace elements (TE) contamination in forested areas of the Itacaiúnas River Watershed (IRW), Brazilian Amazon, arouses growing interest owing to the rapid deforestation and mining activities. In this study, soils (surface, SS; bottom, BS) and stream sediments (SD) from forested/deforested areas of IRW were analyzed with the aim of (1) evaluating the major sources of TE (mainly As, Ba, Cd, Cu, Co, Cr, Hg, Mo, Mn, Ni, Pb, V, and Zn), and (2) examining the soil-sediment TE link related to land-use change and/or geologic factors. Compositional data analysis (CoDA) was used to eliminate data closure issues and the centred log-ratio (clr) transformation yielded better results in Principal Component Analysis (PCA). The TE distribution pattern was significantly different (p < 0.05) between forested and deforested areas, but in both areas the TE distribution pattern is significantly correlated between SS, BS, and SD, indicating a strong lithogenic control. PCA (clr-transformed) identified the major geochemical bedrock signature as Fe-Ti-V-Cu-Cr-Ni, which is nearly similar in soil and sediments. The more accentuated enrichment and the maximum number of anomalies of these elements were found in the Carajás Basin and are highly coincident with mineral deposits/local lithologies without clear indication of anthropogenic contamination from point sources. Besides geogenic factors, deforestation is also affecting TE distribution in the basin. In deforested areas, Mn was significantly enriched in the surface horizon. Furthermore, linear regression analysis shows stronger TE relationships between soils and sediments in deforested areas than in forested ones, reflecting higher erosion in the former. This could be the reason for the relatively higher enrichment of TE (e.g., Fe, Mn, Cu, Cr, Ni) in deforested sediments. The TE contamination using regional background values provides more accurate results than worldwide reference values. Thus, the former should be considered for a more realistic environmental risk assessment in IRW and other forest ecosystems in the Brazilian Amazon.
An Adaptive Classification Model for Predicting Epileptic Seizures Using Cloud Computing Service Architecture

Data science techniques have increasing importance in medical data analysis, including detecting and predicting the probability of contracting a disease. A large amount of medical data is generated close to the patients in the form of a stream, such as data from sensors and medical devices. The distribution of these kinds of data may change from time to time; adaptive Machine Learning (ML) consists of a continuous training process responding to the distribution’s change. Adaptive ML models require high computational resources, which can be provided by cloud computing. In this work, a classification model is proposed to utilize the advantages of cloud computing, edge computing, and adaptive ML. It aims to precisely and efficiently classify EEG signal data, thereby detecting the seizures of epileptic patients using Adaptive Random Forest (ARF). It includes a global adaptive classifier in the cloud master node and a local light classifier in each edge node. In this model, the delayed labels consider missing values, and the Model-based imputation method is used to handle them in the global classifier. Implementing the proposed model on a real huge dataset (CHB-MIT) showed an accurate performance. It has a 0.998 True Negative Rate, a 0.785 True Positive Rate, and a 0.0017 False Positive Rate, which overcomes much of the research in the state-of-the-art.
An Implementation of Face Mask Detection System Based on YOLOv4 Architecture

To solve the problems of low intelligence, low real-time, and poor warning in the face of a complex environment, a face mask detection system based on YOLOv4 architecture is implemented by combining the face mask dataset in the real world. The system can correctly detect faces from images and real-time videos, and judge whether the target face is wearing a mask in a standard way. From the experimental results, the system can distinguish the detected faces with three labels as with a mask, without a mask, and mask worn incorrectly, and achieve a mean Average Precision (mAP) of 87.94&#x0025;. At the same time, the system can realize the dual combination of detection and warning by recording and uploading face data, which is of high academic research value and practical application value.
A U‐snake based deep learning network for right ventricle segmentation

Ventricular segmentation is of great importance for the heart condition monitoring. However, manual segmentation is time-consuming, cumbersome, and subjective. Many segmentation methods perform poorly due to the complex structure and uncertain shape of the right ventricle, so we combine deep learning to achieve automatic segmentation.This paper proposed a method named U-Snake network which is based on the improvement of deep snake together with level set to segment the right ventricular in the MR images. U-snake aggregates the information of each receptive field which is learned by circular convolution of multiple dilation rates. At the same time, we also added dice loss functions and transferred the result of U-Snake to the level set so as to further enhance the effect of small object segmentation. Our method is tested on the test 1 and test 2 datasets in the right ventricular segmentation challenge (RVSC), which shows the effectiveness.The experiment showed that we have obtained good result in the RVSC. The highest segmentation accuracy on the right ventricular test set 2 reached a dice coefficient of 0.911, and the segmentation speed reached 5 fps.Our method, a new deep learning network named U-snake, has surpassed the previous excellent ventricular segmentation method based on mathematical theory and other classical deep learning methods, such as Residual U-net, Inception CNN, and Dilated CNN. However, it can only be used as an auxiliary tool instead of replacing the work of human beings.
Value iteration for simple stochastic games: Stopping criterion and learning algorithm

The classical problem of reachability in simple stochastic games is typically solved by value iteration (VI), which produces a sequence of under-approximations of the value of the game, but is only guaranteed to converge in the limit. We provide an additional converging sequence of over-approximations, based on an analysis of the game graph. Together, these two sequences entail the first error bound and hence the first stopping criterion for VI on simple stochastic games, indicating when the algorithm can be stopped for a given precision. Consequently, VI becomes an anytime algorithm returning the approximation of the value and the current error bound. We further use this error bound to provide a learning-based asynchronous VI algorithm; it uses simulations and thus often avoids exploring the whole game graph, but still yields the same guarantees. Finally, we experimentally show that the overhead for computing the additional sequence of over-approximations often is negligible.