Reviewerly/exHarmony huggingface dataset:
Surgical Scene Segmentation Using Semantic Image Synthesis with a Virtual Surgery Environment

The previous image synthesis research for surgical vision had limited results for real-world applications with simple simulators, including only a few organs and surgical tools and outdated segmentation models to evaluate the quality of the image. Furthermore, none of the research released complete datasets to the public enabling the open research. Therefore, we release a new dataset to encourage further study and provide novel methods with extensive experiments for surgical scene segmentation using semantic image synthesis with a more complex virtual surgery environment. First, we created three cross-validation sets of real image data considering demographic and clinical information from 40 cases of real surgical videos of gastrectomy with the da Vinci Surgical System (dVSS). Second, we created a virtual surgery environment in the Unity engine with five organs from real patient CT data and 22 the da Vinci surgical instruments from actual measurements. Third, We converted this environment photo-realistically with representative semantic image synthesis models, SEAN and SPADE. Lastly, we evaluated it with various state-of-the-art instance and semantic segmentation models. We succeeded in highly improving our segmentation models with the help of synthetic training data. More methods, statistics, and visualizations on https://sisvse.github.io/ .
Learning-Based US-MR Liver Image Registration with Spatial Priors

Registration of multi-modality images is necessary for the assessment of liver disease. In this work, we present an image registration workflow which is designed to achieve reliable alignment for subject-specific magnetic resonance (MR) and intercostal 3D ultrasound (US) images of the liver. Spatial priors modeled from the right rib segmentation are utilized to generate the initial alignment between the MR and US scans without the need of any additional tracking information. For rigid alignment, tissue segmentation models are extracted from the MR and US data with a learning-based approach to apply surface point cloud registration. Local alignment accuracy is further improved via the LC2 image similarity metric-based non-rigid registration technique. This workflow was validated with in vivo liver image data for 18 subjects. The best average TRE of rigid and non-rigid registration obtained with our dataset was at 6.27 ± 2.82 mm and 3.63 ± 1.87 mm, respectively.
Deep Laparoscopic Stereo Matching with Transformers

The self-attention mechanism, successfully employed with the transformer structure is shown promise in many computer vision tasks including image recognition, and object detection. Despite the surge, the use of the transformer for the problem of stereo matching remains relatively unexplored. In this paper, we comprehensively investigate the use of the transformer for the problem of stereo matching, especially for laparoscopic videos, and propose a new hybrid deep stereo matching framework (HybridStereoNet) that combines the best of the CNN and the transformer in a unified design. To be specific, we investigate several ways to introduce transformers to volumetric stereo matching pipelines by analyzing the loss landscape of the designs and in-domain/cross-domain accuracy. Our analysis suggests that employing transformers for feature representation learning, while using CNNs for cost aggregation will lead to faster convergence, higher accuracy and better generalization than other options. Our extensive experiments on Sceneflow, SCARED2019 and dVPN datasets demonstrate the superior performance of our HybridStereoNet.
Predicting input validation vulnerabilities based on minimal SSA features and machine learning

Structured Query Language injection (SQLi) and Cross-Site Scripting (XSS) are the most renowned kinds of input validation vulnerabilities. Of late, vulnerability prediction models based on machine learning have been gaining acceptance in the domain of Web security. Such models offer an easy and effective way of dealing with web application security concerns. However, most of them, in particular, rely on complex graphs generated from source code or regex patterns based on expert knowledge. This paper proposed a method for extracting features from source code and predicting input validation vulnerabilities using machine learning algorithms. The proposed method can extract all features related to the flow of vulnerabilities among the programs and remove the features that are irrelevant to the vulnerability flow. In addition, each vulnerability’s context has been assigned, providing additional data for our model to use in learning about the vulnerability context. Compared to other related methods, the feature extraction method proposed in this paper has been found to have high reusability and better performance. The best model related to the LSTM classifier had a 98.1% recall rate, a 97.9% precision, an accuracy of 98.67%, and a 99.03% area under the curve (AUC) in the test dataset.
A Novel Multi-view Bi-clustering method for identifying abnormal Co-occurrence medical visit behaviors

Abnormal co-occurrence medical visit behavior is a form of medical insurance fraud. Specifically, an organized gang of fraudsters hold multiple medical insurance cards and purchase similar drugs frequently at the same time and the same location in order to siphon off medical insurance funds. Conventional identification methods to identify such behaviors rely mainly on manual auditing, making it difficult to satisfy the needs of identifying the small number of fraudulent behaviors in the large-scale medical data. On the other hand, the existing single-view bi-clustering algorithms only consider the features of the time-location dimension while neglecting the similarities in prescriptions and neglecting the fact that fraudsters may belong to multiple gangs. Therefore, in this paper, we present a multi-view bi-clustering method for identifying abnormal co-occurrence medical visit behavioral patterns, which performs cluster analysis simultaneously on the large-scale, complex and diverse visiting record dimension and prescription dimension to identify bi-clusters with similar time-location features. The proposed method constructs a matrix view of patients and visit records as well as a matrix view of patients and prescriptions, while decomposing multiple data matrices into sparse row and column vectors to obtain a consistent patient population across views. Subsequently the proposed method identifies the corresponding abnormal co-occurrence medical visit behavior and may greatly facilitate the safe operations and the sustainability of medical insurance funds. The experimental results show that our proposed method leads to more efficient and more accurate identifications of abnormal co-occurrence medical visit behavior, demonstrating its high efficiency and effectiveness.
Rotated Object Detection of Remote Sensing Image Based on Binary Smooth Encoding and Ellipse-Like Focus Loss

Remote sensing image object detection has been widely developed in many applications. Objects in remote sensing data have the characteristic of arbitrary directions, which leads to poor detection performance based on horizontal box detectors. To address this issue, a novel rotated object detection model based on binary smooth encoding and ellipse-like focus loss is proposed in this paper. Firstly, a multi-layer feature fusion network with attention mechanism is developed to extract features of multi-scale objects. Then, an anchor free detection module with binary smooth encoding is proposed, which aims to predict the rotated angles of objects. Moreover, an ellipse-like focus loss is proposed to obtain high-quality bounding boxes drawing near the object center. Experimental results on two public remote sensing datasets verify that the proposed method can yield superior detection performance than other related rotated object detection models.
TPSN: Transformer-based multi-Prototype Search Network for few-shot semantic segmentation

Few-shot semantic segmentation aims to segment new objects in the image with limited annotations. Typically, in metric-based few-shot learning, the expression of categories is obtained by averaging global support object information. However, a single prototype cannot accurately describe a category. Meanwhile, simple foreground averaging operations also ignore the dependencies between objects and their surroundings. In this paper, we propose a novel Transformer-based Prototype Search Network (TPSN) for few-shot segmentation. We use the transformer encoder to integrate information between different image regions and then use the decoder to express a category in terms of multiple prototypes. The multi-prototype approach can effectively alleviate the feature fluctuation caused by limited annotation data. Moreover, we use adaptive prototype search during multi-prototype extraction instead of the ordinary averaging operation compared with the previous few-shot prototype framework. This helps the network integrate the different image regions’ information and fuse object features with their dependent background information, obtaining more reasonable prototype expressions. In addition, to encourage the category’s prototypes to focus on different parts and maintain consistency in high-level semantics, we use the diversity and consistency loss to constrain the multi-prototype training. Experiments show that our algorithm achieves state-of-the-art performance in few-shot segmentation on two datasets: PASCAL- 5 i and COCO- 2 0 i . • Multi-prototype gets better segmentation results compared with single-prototype. • Transform annotated mask information to learnable Guide-Embedding can provide more detailed support for few-shot learning. • The communication of image region feature can effectively help the model get context environment context around categories.
Skills Expectations in Cybersecurity: Semantic Network Analysis of Job Advertisements

Cybersecurity jobs are in demand around the country in response to the rapidly growing number of cyberattacks on businesses and government entities. While there is some consensus on core technical skills needed to fill these positions, many hiring employers are not aware of or deemphasize the importance of soft skills in these positions. Using a semantic network analysis of job advertisements, this study sought to answer two research questions: what are the most critical soft skills cybersecurity employers seek? And how are the soft skills related to the hard skills based on industry expectations? This study reports the analysis of 17,929 cybersecurity job advertisements. Our results found that three categories of soft skills emerged: Knowledge management and systems, big data analytical skills, and teamwork and diversity are all highly sought after. In addition, our study findings reinforce a critical role information system is playing in cyber security.
Scalable Causal Structure Learning: Scoping Review of Traditional and Deep Learning Algorithms and New Opportunities in Biomedicine

Causal structure learning refers to a process of identifying causal structures from observational data, and it can have multiple applications in biomedicine and health care.This paper provides a practical review and tutorial on scalable causal structure learning models with examples of real-world data to help health care audiences understand and apply them.We reviewed traditional (combinatorial and score-based) methods for causal structure discovery and machine learning-based schemes. Various traditional approaches have been studied to tackle this problem, the most important among these being the Peter Spirtes and Clark Glymour algorithms. This was followed by analyzing the literature on score-based methods, which are computationally faster. Owing to the continuous constraint on acyclicity, there are new deep learning approaches to the problem in addition to traditional and score-based methods. Such methods can also offer scalability, particularly when there is a large amount of data involving multiple variables. Using our own evaluation metrics and experiments on linear, nonlinear, and benchmark Sachs data, we aimed to highlight the various advantages and disadvantages associated with these methods for the health care community. We also highlighted recent developments in biomedicine where causal structure learning can be applied to discover structures such as gene networks, brain connectivity networks, and those in cancer epidemiology.We also compared the performance of traditional and machine learning-based algorithms for causal discovery over some benchmark data sets. Directed Acyclic Graph-Graph Neural Network has the lowest structural hamming distance (19) and false positive rate (0.13) based on the Sachs data set, whereas Greedy Equivalence Search and Max-Min Hill Climbing have the best false discovery rate (0.68) and true positive rate (0.56), respectively.Machine learning-based approaches, including deep learning, have many advantages over traditional approaches, such as scalability, including a greater number of variables, and potentially being applied in a wide range of biomedical applications, such as genetics, if sufficient data are available. Furthermore, these models are more flexible than traditional models and are poised to positively affect many applications in the future.
Context-Aware Complex Human Activity Recognition Using Hybrid Deep Learning Models

Smart devices, such as smartphones, smartwatches, etc., are examples of promising platforms for automatic recognition of human activities. However, it is difficult to accurately monitor complex human activities on these platforms due to interclass pattern similarities, which occur when different human activities exhibit similar signal patterns or characteristics. Current smartphone-based recognition systems depend on traditional sensors, such as accelerometers and gyroscopes, which are built-in in these devices. Therefore, apart from using information from the traditional sensors, these systems lack the contextual information to support automatic activity recognition. In this article, we explore environmental contexts, such as illumination (light conditions) and noise level, to support sensory data obtained from the traditional sensors using a hybrid of Convolutional Neural Network and Long Short-Term Memory (CNN–LSTM) learning models. The models performed sensor fusion by augmenting low-level sensor signals with rich contextual data to improve the models’ recognition accuracy and generalization. Two sets of experiments were performed to validate the proposed solution. The first set of experiments used triaxial inertial sensing signals to train baseline models, while the second set of experiments combined the inertial signals with contextual information from environmental sensors. The obtained results demonstrate that contextual information, such as environmental noise level and light conditions using hybrid deep learning models, achieved better recognition accuracy than the traditional baseline activity recognition models without contextual information.
Abstractive Summarization of Text Document in Malayalam Language: Enhancing Attention Model Using POS Tagging Feature

Over the past few years, researchers are showing huge interest in sentiment analysis and summarization of documents. The primary reason being that huge volumes of information are available in textual format, and this data has proven helpful for real-world applications and challenges. The sentiment analysis of a document will help the user comprehend the content’s emotional intent. Abstractive summarization algorithms generate a condensed version of the text, which can then be used to determine the emotion represented in the text using sentiment analysis. Recent research in abstractive summarization concentrates on neural network-based models, rather than conjunctions-based approaches, which might improve the overall efficiency. Neural network models like attention mechanism are tried out to handle complex works with promising results. The proposed work aims to present a novel framework that incorporates the part of speech tagging feature to the word embedding layer, which is then used as the input to the attention mechanism. With POS feature being part of the input layer, this framework is capable of dealing with words containing contextual and morphological information. The relevance of POS tagging here is due to its strong reliance on the language’s syntactic, contextual, and morphological information. The three main elements in the work are pre-processing, POS tagging feature in the embedding phase, and the incorporation of it into the attention mechanism. The word embedding provides the semantic concept about the word, while the POS tags give an idea about how significant the words are in the context of the content, which corresponds to the syntactic information. The proposed work was carried out in Malayalam, one of the prominent Indian languages. A widely used and accepted dataset from the English language was translated to Malayalam for conducting the experiments. The proposed framework gives a ROUGE score of 28, which outperformed the baseline models.
Vehicle Re-Identification Based on UAV Viewpoint: Dataset and Method

High-resolution remote sensing images bring a large amount of data as well as challenges to traditional vision tasks. Vehicle re-identification (ReID), as an essential vision task that can utilize remote sensing images, has been widely used in suspect vehicle searches, cross-border vehicle tracking, traffic behavior analysis, and automatic toll collection systems. Although there have been a large number of studies on vehicle ReID, most of them are based on fixed surveillance cameras and do not take full advantage of high-resolution remote sensing images. Compared with images collected by fixed surveillance cameras, high-resolution remote sensing images based on Unmanned Aerial Vehicles (UAVs) have the characteristics of rich viewpoints and a wide range of scale variations. These characteristics bring richer information to vehicle ReID tasks and have the potential to improve the performance of vehicle ReID models. However, to the best of our knowledge, there is a shortage of large open-source datasets for vehicle ReID based on UAV views, which is not conducive to promoting UAV-view-based vehicle ReID research. To address this issue, we construct a large-scale vehicle ReID dataset named VRU (the abbreviation of Vehicle Re-identification based on UAV), which consists of 172,137 images of 15,085 vehicles captured by UAVs, through which each vehicle has multiple images from various viewpoints. Compared with the existing vehicle ReID datasets based on UAVs, the VRU dataset has a larger volume and is fully open-source. Since most of the existing vehicle ReID methods are designed for fixed surveillance cameras, it is difficult for these methods to adapt to UAV-based vehicle ReID images with multi-viewpoint and multi-scale characteristics. Thus, this work proposes a Global Attention and full-Scale Network (GASNet) for the vehicle ReID task based on UAV images. To verify the effectiveness of our GASNet, GASNet is compared with the baseline models on the VRU dataset. The experiment results show that GASNet can achieve 97.45% Rank-1 and 98.51% mAP, which outperforms those baselines by 3.43%/2.08% improvements in terms of Rank-1/mAP. Thus, our major contributions can be summarized as follows: (1) the provision of an open-source UAV-based vehicle ReID dataset, (2) the proposal of a state-of-art model for UAV-based vehicle ReID.
Meta transfer learning-based super-resolution infrared imaging

We propose an infrared image super-resolution method with meta-transfer learning and a lightweight network. We design a lightweight network to learn the map between low-resolution and high-resolution infrared images. We train the network with an external dataset and use meta-transfer learning with an internal dataset that makes the network drop to a sensitive and transferable point. We build an infrared imaging system with an infrared module. The designed network is implemented on a personal computer and the SR image is reconstructed by the trained network. The main contribution of this paper is to adopt a lightweight network and meta-transfer learning method, which obtains infrared super-resolution images with better visual effects. Both numerical and experimental results show that the proposed method achieves the infrared image super-resolution, and the performance of the proposed method is superior to four state-of-art image super-resolution methods. The proposed method has practical application in the image super-resolution of mobile infrared devices.
Automatic Cause–Effect Graph Tool with Informal Korean Requirement Specifications

In requirement engineering, it is a very important issue to generate test cases with natural language automatically. However, no test case tools deal with informal Korean requirement specifications. In the Korean military software system and airspace industrial area, it is strongly suggested to automatically make just 30% of all possible test cases with requirements. Unlike the previous approaches, we adapted Gary E. Mogyorodi’s cause-effect graphing approach and the model-driven architecture (MDA) approach for automatic test case generation with natural language. In order to generate test cases with informal Korean requirement specifications, we propose an automatic cause–effect tool as an intermediate model for (1) simplifying complicated requirements; (2) modeling the C3Tree (that is, condition and result); (3) identifying incomplete requirements; (4) constructing causes, effects, and relationships; and (5) integrating with two units (that is, similar causes or effects) to remove redundant requirements. We evaluated the accuracy of two generated cause–effect graphs in two ways. With our approach, we can also remove requirement redundancy.
Security Vulnerabilities and Countermeasures of Smart Contracts: A Survey

Recently, blockchain-based smart contracts have become popular because of their immunity, decentralization, and security insurance in a trustless environment. However, they are highly susceptible to various security risks, so it is necessary to systematically investigate existing security countermeasures and use unified evaluation criteria. There have been some surveys on the security of blockchain-based smart contracts, albeit they have failed to conduct a reasonable analysis of existing security solutions with a unified criterion. Therefore, this article comprehensively investigates the latest work on security of blockchain-based smart contracts. For better understanding, we will design a security model that represents blockchain-based smart contracts. Furthermore, we analyze the security requirements of blockchain and smart contracts and used these requirements as evaluation criteria to study the works under investigation. Finally, based on the results of the analysis, we present a series of open research issues and future directions to stimulate research work on protecting blockchain-based smart contracts.