Reviewerly/exHarmony huggingface dataset:
Recent Progress of Using Knowledge Graph for Cybersecurity

In today’s dynamic complex cyber environments, Cyber Threat Intelligence (CTI) and the risk of cyberattacks are both increasing. This means that organizations need to have a strong understanding of both their internal CTI and their external CTI. The potential for cybersecurity knowledge graphs is evident in their ability to aggregate and represent knowledge about cyber threats, as well as their ability to manage and reason with that knowledge. While most existing research has focused on how to create a full knowledge graph, how to utilize the knowledge graph to tackle real-world industrial difficulties in cyberattack and defense situations is still unclear. In this article, we give a quick overview of the cybersecurity knowledge graph’s core concepts, schema, and building methodologies. We also give a relevant dataset review and open-source frameworks on the information extraction and knowledge creation job to aid future studies on cybersecurity knowledge graphs. We perform a comparative assessment of the many works that expound on the recent advances in the application scenarios of cybersecurity knowledge graph in the majority of this paper. In addition, a new comprehensive classification system is developed to define the linked works from 9 core categories and 18 subcategories. Finally, based on the analyses of existing research issues, we have a detailed overview of various possible research directions.
QuALITY: Question Answering with Long Input Texts, Yes!

To enable building and testing models on long-document comprehension, we introduce QuALITY, a multiple-choice QA dataset with context passages in English that have an average length of about 5,000 tokens, much longer than typical current models can process.Unlike in prior work with passages, our questions are written and validated by contributors who have read the entire passage, rather than relying on summaries or excerpts.In addition, only half of the questions are answerable by annotators working under tight time constraints, indicating that skimming and simple search are not enough to consistently perform well.Our baseline models perform poorly on this task (55.4%) and significantly lag behind human performance (93.5%).
A context-scale-aware detector and a new benchmark for remote sensing small weak object detection in unmanned aerial vehicle images

Due to the fast development of imaging sensors used in remote sensing, high-resolution images can increasingly exhibit fine-grained information on the Earth’s surface, which makes detecting real-world small-scale, weak-feature-response geospatial targets possible. Detecting these small weak targets is of great significance in some applications. Although remarkable efforts have been made to develop small weak object detection, existing works mainly focus on processing satellite images. Unmanned aerial vehicle (UAV) remote sensing images have very high resolutions and short revisit times, thus making them a novel kind of data for Earth observation, and are more rarely studied. Meanwhile, due to the characteristics of UAV images, some problems, such as high intra-class variations, complex contexts, and noise, are pronounced. To promote the remote sensing detection of small weak objects in UAV images, this paper proposes a 10-category UAV object detection dataset, namely UAVOD-10, 1 and a novel context-scale-aware detector, namely, CSADet. The objects in UAVOD-10 are sufficiently varied and affected by imaging conditions and their contexts. Some of them, such as cable towers, wells, and cultivation mesh cages, have small scales and weak feature responses, making them difficult to recognize. To locate these kinds of objects efficiently, CSADet first utilizes a context-aware module that can jointly explore valuable local and global contexts and then applies a multi-scale feature refinement module to extract and share all the levels of the features. Extensive experiments on the proposed UAVOD-10 dataset demonstrate its remarkable detection performance.
Hepatitis C Virus Detection Model by Using Random Forest, Logistic-Regression and ABC Algorithm

This study proposes an automatic classifier for detecting the multiclass probabilities of hepatitis C virus (HCV) incidence based on patients&#x2019; blood attributes. The purpose of this study is to establish an artificial intelligence based model that can identify HCV patients and detect the disease in early stage for future treatments. This model can be applied by using clinical data and keeps the performance from imbalanced datasets. This objective was achieved using a cascade two-stage method combining the random forest (RF) and logistic regression (LR) algorithms. Two models were trained by applying the RF (Model 1) and LR (Model 2) to raw and preprocessed data, respectively. The artificial bee colony (ABC) algorithm was then used to determine the optimal threshold value required for filtering and separation, that is, the optimal combination of both models. The two-stage mixing algorithm combines algorithms of different search dimensions, thus integrating the strengths of those algorithms. The critical threshold value for separating Model 1 and Model 2 was obtained through an optimized search using the ABC algorithm. After conducting 10-fold Monte Carlo cross-validation experiments 50 times (for mean values), data from the recent pandemic were used to verify the proposed method. To evaluate the quantitative results, indicators, such as prediction accuracy, precision, recall, F1-score, and Matthews correlation coefficient, were compared with those of the latest algorithms used in relevant fields. The results indicate that the proposed model, named Cascade RF-LR (with SMOTE), can be used to detect the multiclass probabilities of HCV incidence using the ABC algorithm, thereby improving the effectiveness of relevant treatments.
TPET: Two-stage Perceptual Enhancement Transformer Network for Low-light Image Enhancement

Low-light images captured under low light or backlight conditions can suffer from different types of degradation such as low visibility, strong noise and color distortion. In this paper, to solve the degradation problem of low-light images, we propose Two-stage Perceptual Enhancement Transformer Network(TPET) for Low-light Image Enhancement by combining the advantages of local spatial perception of convolutional neural network and global spatial perception of transformer. The method is generally divided into two stages: feature extraction stage and detail fusion stage. First, in the feature extraction stage, the encoder composed of transformers performs global feature extraction and expands the receptive field. Since the transformer lacks the ability to capture local features, we introduce a perceptual enhancement module (PEM) to improve the interaction of local and global feature information. Second, between the corresponding encoding and decoding blocks in each layer, a feature fusion block (FFB) is introduced to compensate the feature information at different scales to improve the reusability of features and enhance the stability of the network. In addition, between the two stages, the local information features are redistributed and the network supervision capability is improved by introducing a self-calibration module (SCM). In the detail fusion stage, in order to further preserve the details of textural features of the image, we designed a detail enhancement unit (DEU) for recovering high-resolution enhanced images. Through qualitative comparison and quantitative analysis, our method outperforms other low-light image enhancement methods in terms of subjective visual effects and objective metrics values.
Cluster Analysis with K-Mean versus K-Medoid in Financial Performance Evaluation

Nowadays there is a large amount of information at our disposal, which is increasing day by day, and right now the question is not whether we have a method to process it, but which method is most effective, faster and best. When processing large databases, with different data, the formation of homogeneous groups is recommended. This paper presents the financial performance of Hungarian and Romanian food retail companies by using two well-known cluster analyzing methods (K-Mean and K-Medoid) based on ROS (Return on Sales), ROA (Return on Assets) and ROE (Return on Equity) financial ratios. The research is based on two complete databases, including the financial statements for five years of all retail food companies from one Hungarian and one Romanian county. The hypothesis of the research is: in the case of large databases with variable quantitative data, cluster analysis is necessary in order to obtain accurate results and the method chosen can bring different results. It is justified to think carefully about choosing a method depending on the available data and the research aim. The aim of this study is to highlight the differences between the results of these two grouping procedures. Using the two methods we reached different results, which means a different evaluation of financial performance. The results demonstrate that the method chosen for grouping may influence the assessment of the financial performance of companies: the K-Mean method produces a greater variety of groups and the range of results obtained after grouping is larger; whereas, the group distribution and the results obtained by the K-Medoid method are more balanced.
Earthquake early warning in countries where damaging earthquakes only occur every 50 to 150 years – The societal perspective

Earthquake Early Warning (EEW) systems can trigger actions just before the shaking hits and hence mitigate the impact of damaging earthquakes. Recent studies have mainly focused on technical challenges, performance or on the attitudes of the public towards EEW systems in countries where EEW systems are already in operation or where damaging earthquakes are frequent. Building on these efforts, from the societal perspective we assess how different message elements influence the intention to take action and ability to grasp relevant information within a few seconds. Further, we explore the public attitudes towards EEW systems in a country where damaging earthquakes are expected less frequently. In a survey (N = 596, between-subjects experiment) targeting the Swiss public, we assess EEW system preferences and test different versions of EEW messages to identify elements which trigger people to take immediate actions and can be correctly recalled. Our main findings are that i) the public attitudes in countries with moderate seismic hazard are similar to attitudes in countries with high seismic hazard; ii) pictograms trigger people to protect themselves on the spot whereas maps prompt the public to look for further information or to warn others; iii) the designs preferred by the public are not always those that actually trigger them to take action; and iv) people tend to react proportionally to the hazard level. We show that insights from studies focusing on societal issues related to EEW systems can help operators define technical settings and to address specific concerns or likely misconceptions in education campaigns.
Experimental and numerical full-field displacement and strain characterization of wind turbine blade using a 3D Scanning Laser Doppler Vibrometer

• First work of measuring the full-field displacement and strain of a wind turbine blade with a 3D Scanning Laser Doppler Vibrometer (SLDV). • High spatial resolution scan of the entire blade with over 800 3D points. • First work of correlating the full-field strain measurement with the finite element model having anisotropic composite material. • Establish the relationship between the internal blade structural configuration and the global strain distribution measurement. Full-field dynamic measurement is desirable and useful for many purposes including model validation, or experimental characterization of test articles for which a finite element model is not available. Surface full-field strain measurement and test-model correlation have been performed on a small-size isotropic structure with a non-contacting 3D Scanning Laser Doppler Vibrometer (SLDV), which has an advantage over strain gauge tests with measurements at only a few discrete points. However, the application on large-size anisotropic structures has not been explored. This is the first work using the 3D SLDV for measuring the full-field displacement and strain Operational Deflection Shapes (ODSs) of a large composite structure, a 4.2-meter wind turbine blade. The displacement and strain measurement results are also correlated to the corresponding predictions of the blade composite finite element model in both a visual and quantified manner. The correlation is demonstrated for both low-frequency fundamental modes, but also for complex, highly-coupled high-frequency modes, which is challenging for other optical methods. Correlation results show a high degree of agreement between measured and numerically predicted displacements and strains, demonstrating the validity of the 3D SLDV measurement and the developed finite element model of the composite blade. The strain shapes are also compared with the displacement shapes and the blade structural configuration to reveal the effect of the blade structure on the strain distribution. The full-field displacement and strain identified and the proposed measurement and correlation technique would benefit wind turbine blade designers by providing new experimental tools to evaluate blade structural performance and reliability and provides a useful reference for blade structural designers.
A Federated Learning Framework for Breast Cancer Histopathological Image Classification

Quantities and diversities of datasets are vital to model training in a variety of medical image diagnosis applications. However, there are the following problems in real scenes: the required data may not be available in a single institution due to the number of patients or the type of pathology, and it is often not feasible to share patient data due to medical data privacy regulations. This means keeping private data safe is required and has become an obstacle in fusing data from multi-party to train a medical model. To solve the problems, we propose a federated learning framework, which allows knowledge fusion achieved by sharing the model parameters of each client through federated training rather than sharing data. Based on breast cancer histopathological dataset (BreakHis), our federated learning experiments achieve the expected results which are similar to the performances of the centralized learning and verify the feasibility and efficiency of the proposed framework.
A Survey of Convolutional Neural Network in Breast Cancer

For people all over the world, cancer is one of the most feared diseases. Cancer is one of the major obstacles to improving life expectancy in countries around the world and one of the biggest causes of death before the age of 70 in 112 countries. Among all kinds of cancers, breast cancer is the most common cancer for women. The data showed that female breast cancer had become one of the most common cancers.A large number of clinical trials have proved that if breast cancer is diagnosed at an early stage, it could give patients more treatment options and improve the treatment effect and survival ability. Based on this situation, there are many diagnostic methods for breast cancer, such as computer-aided diagnosis (CAD).We complete a comprehensive review of the diagnosis of breast cancer based on the convolutional neural network (CNN) after reviewing a sea of recent papers. Firstly, we introduce several different imaging modalities. The structure of CNN is given in the second part. After that, we introduce some public breast cancer data sets. Then, we divide the diagnosis of breast cancer into three different tasks: 1. classification; 2. detection; 3. segmentation.Although this diagnosis with CNN has achieved great success, there are still some limitations. (i) There are too few good data sets. A good public breast cancer dataset needs to involve many aspects, such as professional medical knowledge, privacy issues, financial issues, dataset size, and so on. (ii) When the data set is too large, the CNN-based model needs a sea of computation and time to complete the diagnosis. (iii) It is easy to cause overfitting when using small data sets.
Example-based explanations for streaming fraud detection on graphs

Fraud detection is one of the most important tasks in Web platforms such as e-commerce, social media, network security, and financial systems. To prevent fraudulent actions from misleading customers or causing significant losses for businesses, various fraud detection methods have been proposed in recent years. However, research on fraud definitions, characteristics, and behaviours has been limited to which users, items, and transactions are considered fraudulent rather than why these entities have been classified as such. This inhibits effective validation of the detected frauds as well as countermeasure design. In this paper, we argue that explanations for discovered frauds may be provided in terms of prior identified frauds. A large variety of comparable frauds would assist investigators to generalise, allowing them to grasp the characteristics that are significant for fraud detection. Feature-annotated graphs are frequently used to detect the type of fraud in which fraudsters commonly interact with a large number of benign users to conceal themselves. Given a fraud subgraph, we propose a query-by-example approach for indexing and extracting the k most similar and diverse fraud subgraphs from prior frauds. To achieve an efficient and adaptive realisation of the approach in a streaming setting, we present a novel graph representation learning technique and discuss the implementation considerations. Comparing our study against baseline techniques revealed that our approach outperforms them in delivering meaningful explanations for various fraud camouflage behaviours.
Lung CT image synthesis using GANs

Biomedical engineering has been targeted as a potential research candidate for machine learning applications, with the purpose of detecting or diagnosing pathologies. However, acquiring relevant, high-quality, and heterogeneous medical datasets is challenging due to privacy and security issues and the effort required to annotate the data. Generative models have recently gained a growing interest in the computer vision field due to their ability to increase dataset size by generating new high-quality samples from the initial set, which can be used as data augmentation of a training dataset. This study aimed to synthesize artificial lung images from corresponding positional and semantic annotations using two generative adversarial networks and databases of real computed tomography scans: the Pix2Pix approach that generates lung images from the lung segmentation maps; and the conditional generative adversarial network (cCGAN) approach that was implemented with additional semantic labels in the generation process. To evaluate the quality of the generated images, two quantitative measures were used: the domain-specific Fréchet Inception Distance and Structural Similarity Index. Additionally, an expert assessment was performed to measure the capability to distinguish between real and generated images. The assessment performed shows the high quality of synthesized images, which was confirmed by the expert evaluation. This work represents an innovative application of GAN approaches for medical application taking into consideration the pathological findings in the CT images and the clinical evaluation to assess the realism of these features in the generated images.
Opening up Open World Tracking

Tracking and detecting any object, including ones never-seen-before during model training, is a crucial but elusive capability of autonomous systems. An autonomous agent that is blind to never-seen-before objects poses a safety hazard when operating in the real world - and yet this is how almost all current systems work. One of the main obstacles towards advancing tracking any object is that this task is notoriously difficult to evaluate. A benchmark that would allow us to perform an apples-to-apples comparison of existing efforts is a crucial first step towards advancing this important research field. This paper addresses this evaluation deficit and lays out the landscape and evaluation methodology for detecting and tracking both known and unknown objects in the open-world setting. We propose a new benchmark, TAOOW. Tracking Any Object in an Open World, analyze existing efforts in multi-object tracking, and construct a baseline for this task while highlighting future challenges. We hope to open a new front in multi-object tracking research that will hopefully bring us a step closer to intelligent systems that can operate safely in the real world.
Mapping soil available copper content in the mine tailings pond with combined simulated annealing deep neural network and UAV hyperspectral images

Improper discharge of slag from mining will pollute the surrounding soil, thereby affecting the ecology and becoming an important global problem. The available copper (ACu) content in polluted soil is an important factor affecting plant growth and development. When investigating a large area of soil with ACu, manual sampling by points and inspection are mainly used, due to the heterogeneity of soil, the efficiency and accuracy are lower. The Unmanned aerial vehicle (UAV) equipped with a hyperspectral sensor as a remote sensing technology is widely used in soil indicator monitoring because of its rapid and convenience. Meanwhile, using the relationship between soil organic matter and available copper has the potential to predict available copper. In this study, we selected the study area with tailings area in the Jianghan Plain of China and used a UAV equipped with a hyperspectral sensor to predict ACu and soil organic matter (SOM) in the soil with two datasets. Firstly, 74 soil samples were collected in the study area, and the ACu and SOM of the soil samples were determined. Second, a hyperspectral image of the study area is obtained using a UAV equipped with a hyperspectral sensor. Thirdly, we combine hyperspectral data with competitive adaptive reweighted sampling (CARS) to obtain feature bands and utilize simulated annealing deep neural network (SA-DNN) to generate estimation models. Finally, maps of the distribution of ACu and SOM in the area were generated using the model. In two datasets, the model of ACu with R2 values both are 0.89, and R2 on the model of SOM is 0.89 and 0.88. The results show that the combination of UAV hyperspectral imagery with the SA-DNN model has good performance in the prediction of organic matter and available copper, which is helpful for soil environmental monitoring.
Identity-Based Matchmaking Encryption from Standard Assumptions

In this work, we propose the first identity-based matchmaking encryption (IB-ME) scheme under the standard assumptions in the standard model. This scheme is proven to be secure under the symmetric external Diffie-Hellman (SXDH) assumption in prime order bilinear pairing groups. In our IB-ME scheme, all parameters have constant number of group elements and are simpler than those of previous constructions. Previous works are either in the random oracle model or based on the q-type assumptions, while ours is built directly in the standard model and based on static assumptions, and does not rely on other crypto tools. More concretely, our IB-ME scheme is constructed from a variant of two-level anonymous IBE. We observed that this two-level IBE with anonymity and unforgeability satisfies the same functionality of IB-ME, and its security properties cleverly meet the two requirements of IB-ME (Privacy and Authenticity). The privacy property of IB-ME relies on the anonymity of this two-level IBE, while the authenticity property is corresponding to the unforgeability in the 2nd level. This variant of two-level IBE is built from dual pairing vector spaces, and both security reductions rely on dual system encryption.