Reviewerly/exHarmony huggingface dataset:
Geometric modeling of aortic dissections through convolution surfaces

Cardiovascular diseases are one of the strongest burdens in healthcare. If misdiagnosed, they can lead to life-threatening complications. This is especially true for aortic dissections, which may require immediate surgery depending on the categorization and still lead to late adverse events. Aortic dissection occurs when the aortic duct splits into two blood streams, the true and false lumina. The morphological characteristics of the aorta are therefore crucial for a clinician and provide vital support since they can be used to extract significant information for surgery and treatment planning. In this work, we revive a successful modeling technique – convolution surfaces – to model the lumina in aortic dissections. The skeleton of the lumina and local radial information are used to represent the true and the false lumen through convolution of local segments. Additionally, we introduce an optimization strategy based on a genetic algorithm to create the separation caused by the dissection flap.
NLP and Logic Reasoning for Fully Automating Test

Test automation allows automatizing some repetitive and tedious but essential tasks in a formalized testing process already in place, or to achieve additional testing that would be complicated manually. However, the automated testing software tools available today are typically used to execute a test case manually written and identified. However, it is a highly challenging task due to: (1) the large variability in structure of functional specification documents; (2) and the inter and intra observer variability across testers. In this work, we propose a novel automated test framework introducing three major contributions: (1) Modeling the interactions across all process (design, planning, and execution). Specifically, our framework permits the use of textual functional specifications to automate test projects. (2) Our framework automatizes test projects using Machine Learning (ML) and Natural Language Processing (NLP). Specifically, it automatically extracts automated test scenarios from functional specifications requirements. (3) The proposed method captures shared and complementary information between different processes. We evaluated our framework using 300 pages of project specification. We show that our framework is robust for the standardization of specification, the automatically extraction of test scenarios and the identification of automating scenarios.
Anti-Interference Test Technology of Transformer Noise

Abstract In order to enhance the transformer noise test accuracy, an anti- interference method based on wavelet packet decomposition and spectral subtraction algorithm is proposed in this paper. The tested original noise signal is decomposed with the wavelet packet method. The occurring time and frequency bands of the noisy signals are observed. In these bands, the noisy signals are further processed by spectral subtraction method. Finally, the pure transformer noise is obtained with wavelet packet reconstruction. The anti-interference effects of transformer noises in the conditions of various interference sources and signal to noise ratios are simulated. The result shows that the proposed method is effective in restraining the influence of diverse burst intermittent environmental noises on the transformer noise test result.
A Quantitative Analysis for Breast Cancer Prediction Using Artificial Neural Network and Support Vector Machine

The medical data is increasing rapidly day by day. The number of patients of different disease is rising, and it is difficult for the radiologists to analyze the data, detect, and predict the disease with accurate result. It is important to achieve better performance and classify the disease using different methodologies as database is large. Hence, the review of different state-of-the-art techniques using machine learning and deep learning algorithms is included. The literature is best for the classification of breast cancer and different medical images. The diagnosis and prediction are done using training (80%) and testing (20%) samples on benchmark dataset. Both artificial neural network and support vector machine are compared using parameters, i.e., accuracy, precision, and recall. Experimental results show that SVM is better compared to ANN.
Feature Compensation Network Based on Non-Uniform Quantization of Channels for Digital Image Manipulation Forensics

Nowadays, with the popularity of image editing software and technology, it has become very easy to manipulate an image. Maybe it has serious consequences and adverse effects because of dishonest information. Digital Image Manipulation Forensics (DIMF) has become an important research direction in order to confirm the authenticity of images, detect the manipulations that images have undergone, and avoid the misuse of image editing. Currently, most DIMF target the detection of multiple image manipulations with fixed parameters. However, we consider to detect image manipulation in a more complex scenario where the parameters are chosen to be arbitrary. In this paper, we propose a Feature Compensation Network (FCNet) based on non-uniform quantization of channels. Briefly, it contains three important parts: (1) feature enhancement block, which extracts and enhances valid information from low-level features and eliminates semantic gaps between them and the high-level features. (2) sensitivity estimation block, which obtains the importance coefficients of each channel and guides the non-uniform quantization of low-level features. (3) adaptive average pooling, which keeps the resolution of low-level features and high-level features consistent and ensures that subsequent feature fusion properly. Through extensive experiments, we have demonstrated the effectiveness of the proposed method on detecting multiple image manipulations.
Research on the Construction of College Football Classroom Practice Teaching System Model Based on Big Data Analysis

With the rapid development of information technology, the traditional single classroom teaching and passive learning methods of students can no longer meet the needs of all-round development of college students, and its urgent need to integrate with information technology. This article is aimed at the problem of lagging feedback on training results in the traditional teaching model, teachers' active control, students' passive obedience, ignoring the development of students' personality in college football classrooms, and the inability to carry out personalized tracking and quantitative improvement of the training process of students' related abilities. We constructed a college football classroom practice teaching system model based on big data analysis from the perspectives of establishing big data teaching resources, and implementing personalized resource recommendation, optimizing the traditional teaching process, integrating quantitative training, measurement and recording, implementing quantitative intervention, etc. Colleges and universities have carried out experimental observations. Through continuous observation and comparison, it is found that college football classroom practice teaching under big data is more conducive to improving students' football skills and theoretical level than traditional teaching. This model makes full use of the advantages of big data and the combination of practical teaching methods, which can bring students a different learning experience and obtain good teaching effects. It has guiding and reference significance for college football practical teaching.
3D Modeling and Determination of Factors Responsible for Zinc‐Lead Mineralization in the Mehdiabad Deposit, Central Iran, based on Statistical Analysis of Geochemical Data

Abstract Although the Mehdiabad zinc‐lead deposit is one of the most well‐known deposits in the central Iran structural zone, the genesis of the deposit remains controversial. The host rock of the ore is a dolomitic limestone of the Lower Cretaceous Taft Formation. In the two main orebodies of the deposit, which includes the Black Hill and East Ridge ore zones, the oxide and sulfide ores are observed at the surface and at depth, respectively. The elements Zn, Fe, Mn and Mg are more abundant in the East Ridge ore zone (in both sulfide and oxide ores), with Ba, Pb, Ag and Cu being more abundant in the Black Hill oxide ore. Based on the distribution of elements and their correlation with each other in these ore zones, the elements are divided into three general groups, that of terrigenous elements, chemically‐deposited elements and ore‐forming (hydrothermally deposited) elements, a division that is supported by the results of factor analyses. The spatial distribution of elements is jointly affected by contact with host rocks, the boundary of oxide‐sulfide ores and fault zones. The main factors governing the distribution of elements are the mechanical transfer of detrital sediments, chemical sedimentation, transfer by hydrothermal fluids, oxidation and surface dissolution, all of which affected the spatial distribution of elements. The ore‐forming elements are mostly affected by hydrothermal fluids and oxidation. This study not only provides additional information about the genesis of the Mehdiabad deposit, but also could assist in the exploitation of ore and further exploration purposes. The results of this study can aid in the exploration and exploitation of the Mehdiabad deposit and similar deposits in the region.
Underwater Image Enhancement Based on Intrinsic Images

The complex and changeable underwater environment and light conditions lead to quality degradation problems of underwater image in different degrees. Therefore, underwater image enhancement is an important and challenging task in computer vision. Relying on a powerful visual system, human beings have the ability to restore the real scene from a degraded image, and estimate the object material and light situation of the scene in the image. Inspired by this, we believe that separating the underwater lighting effect from the degraded image to produce enhanced result, is an effective method for underwater image enhancement. We utilize the powerful fitting capabilities of CNN to estimate the illumination image, separate them from underwater image and obtain reflectance image, and we treat the reflectance image as the final enhanced underwater image. Our underwater image enhancement model was implemented by leveraging the light transfer method, and well results were achieved state-of-the-art performance on both public UIEB [15] and EUVP [11] datasets.
Taming Combinatorial Challenges in Clutter Removal

We examine an important combinatorial challenge in clearing clutter using a mobile robot equipped with a manipulator, seeking to compute an optimal object removal sequence for minimizing the task completion time, assuming that each object is grasped once and then subsequently removed. On the structural side, we establish that such an optimal sequence can be NP-hard to compute, even when no two objects to be removed have any overlap. Then, we construct asymptotically optimal and heuristic algorithms for clutter removal. Employing dynamic programming, our optimal algorithm scales to 40 objects. On the other hand, for random clutter, fast greedy algorithms tend to produce solutions comparable to these generated by the optimal algorithm.
Efficient Traceable Attribute-Based Signature With Update-Free Revocation For Blockchain

Abstract Attribute-based signature (ABS) allows signers with a set of attributes to sign messages anonymously using a specific signing policy. However, previous schemes suffer from some efficiency issues which are not widely applied on the blockchain. In this paper, we investigate ABS regarding its features and efficiency in the blockchain setting and provide our solution correspondingly. To solve the revocation problem of ABS in a more efficient manner, we introduce the update-free revocation function. Instead of the passive attribute expiration approaches, we take the active method to ensure that no parameter updates are required by users after the execution of the revocation function. In terms of efficiency, we first address the problem that the signer has to provide proof for all attributes in the predicate for privacy, which is one of the efficiency bottlenecks for ABS. By taking advantage of the blockchain architecture, we propose a new solution which can achieve the constant signature size and verification cost, while the signing cost can be greatly reduced. The corresponding security levels are satisfied according to their strict criteria. A generic construction as well as an instantiation are provided which is provably secure in the standard model satisfying the newly defined formal security definitions. Finally, a purer primitive is discussed.
Real-Time Multi-person Multi-camera Tracking Based on Improved Matching Cascade

In a small-scale distributed multi-camera system like video surveillance system of a museum, shopping mall, plaza, etc., or advanced driving assistance system, real-time multi-person tracking is essential for public and pedestrian safety consideration in the smart security system. In this paper, a real-time multi-person multi-camera tracking framework is presented, which is compatible with both overlapping and non-overlapping views. Since cameras have different orientations and exposures, false matching occurs frequently when people cross the camera boundaries or reenter the same camera. To deal with this challenge, an improved multi-person multi-camera matching cascade scheme is proposed, which can increase the accuracy of inter-camera person re-identification (Re-ID) by taking advantage of association priorities of targets and features. Besides, the proposed method can deal with the occlusion of people and variation of appearance features. Experiments are implemented with overlapping and non-overlapping videos, and results show that the proposed method has robust performance in different situations.
Practically Implementation of Information Loss: Sensitivity, Risk by Different Feature Selection Techniques

The modern perspective to deal with bulky data generations schemes of latest technologies in terms of dimensionality and sample size to extract meaningful information also to support automated knowledge discovery and pattern recognition process form datasets a lot of Data Mining (DM) and Machine Learning (ML) techniques developed.In each dataset features are the key factors for machine learning task.In modern research mindset classification algorithms are focused to get high accuracy by taking in account prior features and less focus on features having low characteristic values.In this paper we focused on those features which are usually ignored in selection phase as low scale features which may decrease model performance but in future for the most sensitive scenarios will focus on minor information which will alert about performance fluctuation in practical implementation of that model.For practical verification of our concept, we implemented rule-based classification algorithms and different features selection techniques with 3 search methods using WEKA data mining tool.The experimental results show that the fewer selected features provide high accuracy i.e > 90% in some cases having less focus on specificity.
3D Pose Prediction: Unsupervised Adversarial Network With Human Geometric Constraint

2D human pose extraction has been completed by multiple frameworks, such as [1], [2], [3], [5], [6], [7], [8], [9], [1]0,[1][1] and other detectors. In this paper, after comparing various 2D detectors, 1.7 version of openpose, the most cutting-edge of CMU, is adopted as the 2D pose detector of this paper for accuracy. As for the transformation from 2D joint pose to 3D pose, Pavllo et al. [4] was referred to, while some modifications were made to the errors still existing in the actual output results. We use the classic GAN[23] model, and the network adopts unsupervised training mode. On this basis, We have added a constraint network to the network layer design to make the 3D human pose predicted by the original confrontation generation network obtain a higher accuracy, and in addition to the generation network we use strong geometric constraints on the key points of the human body which cannot be trained on the network(Such as elbows and knees), the entire human bones are contracted in millimeters, so that the predicted 3D posture will not be too distorted. Experiments have proved that such a method can obtain a state-of-the-art low-error result. The unsupervised network structure makes the framework not dependent on the huge annotated 3D data set. We only use the 3D annotation dataset in the final verification part, the 3D pose data set is provided with real human body pictures from the official website of Human3.6m [13]. Finally, the effective 3D prediction results were projected into the skeleton action Data of Vocaloid Motion Data(VMD)[37]and used to obtain The 3D animation corresponding to 2D poses in the Skeleton editing engine of MikuMikuDance[38].
Aerial image object detection based on improved YOLOv5

Object detection from aerial images has been a popular research field recently. The task, i.e., detecting objects from aerial images, is difficult due to the relatively large image dimensions and various directions of objects. In this paper, to improve the detection ability of small targets, we use mosaic for data enhancement during data preprocessing. A multi-scale test is performed on the test set to make the test result more accurate. In addition, an improved YOLOv5 algorithm, which introduces the circular smoothing label (CSL) angle processing method into YOLOv5, is proposed to detect rotating objects from aerial images. Experimental results on the DOTA-v2.0 dataset show that the proposed method is effective.
Learning Unsupervised Side Information for Zero-Shot Learning

Zero-Shot Learning aims to recognize unseen class images that do not appear in training, which is attracting more and more research interests in recently years. Side information is an important key to ZSL since it transfers the knowledge between seen and unseen classes. Human annotated attribute, as the most popular side information, need much human effort and time consumption during data collection. While unsupervised side information such as word2vec is not performing well since they lack the representation ability for visual information. In this paper, we propose to use CLIP features, which is learned with image and natural language pairs without human efforts, to perform ZSL. Extensive experiments on two benchmark datasets, AWA2 and CUB, demonstrates that our method is achieving impressive accuracy gain over word2vec, even beats human attributes in some circumstances.
Feature Subset Selection Using Genetic Algorithm with Aggressive Mutation for Classification Problem

In this work, the algorithmic approaches to Feature Subset Selection (FSS) are reviewed. FSS is the technique of selecting a subset of relevant features for building parsimonious models. One successful algorithm in selecting relevant features in Brain-Computing Interface is the Genetic Algorithm with Aggressive Mutation (GAAM). We implemented a scikit-learn compatible library for GAAM and determined its applicability with classification tasks in general. Identifying relevant features in a predictive modeling task improves the interpretability of the model, reduces its complexity and the time requirement for training.
Prioritization of model smell refactoring using a covariance matrix-based adaptive evolution algorithm

The refactoring process enhances the software design by modifying the structure of design parts impaired with model smells without altering the overall software behavior. However, handling these smells without proper prioritization will not produce the anticipated effects. In this paper, we solve the prioritization of the model smell refactoring using a multi-objective optimization (MOO) algorithm called the multi-objective (MO) covariance matrix adaptation evolution strategy (MOCMA-ES). Our formulation relies on the refactoring of unified modeling language (UML) class diagrams to mitigate the negative effect of design smells. We treat the prioritization problem as a real-valued MOO where we propose novel data encoding procedures. We use two conflicting objectives, quality, and maintainability, to balance the refactoring. We first build a new solution representation that guarantees smell fixing and eliminates the rejection limitation. Furthermore, we suggest a custom mapping scheme to properly encode real-valued quantities using unique representations. For performance evaluation purposes, we developed a large custom dataset with more than 30,000 class records, using seven popular open-source software projects. A novel relative coverage metric is proposed to mitigate the limitations associated with the standard coverage. For benchmarking purposes, we also consider an improved version of the nondominated sorting genetic algorithm (NSGA-II(. The reported performance scores confirm the superiority of the MOCMA-ES algorithm over NSGA-II. The former successfully identified the refactoring sequences that lead to the best improvements in software quality and maintainability while it is able to fix all identified design smells. These improvements are quantified in terms of hypervolume, coverage, spacing metrics, and execution time. The MOCMA-ES attained the highest average maximum quality score of 1149 while keeping the average maintainability at the lowest score of 13.8. In all experiment settings, the proposed solution leads to longer refactoring sequences at no additional computational cost.
Rapid Automatic Keyword Extraction and Word Frequency in Scientific Article Keywords Extraction

Research is one activity that has to be conducted by lecturers or researchers. In fact, they usually deliver articles regarding the research that has been conducted. Scientific articles that are produced by researchers have keywords to represent their contents. The selection of keywords that have been used in scientific articles sometimes does not match the whole content. Selection of keywords will be easier if assisted by a keyword extraction system. Due to the keywords that can be used in searching articles on search engines as a reference, keywords take an important part in the article. Some methods that can be used to extract keywords from one article are word frequency and RAKE (Rapid Automatic Keyword Extraction). Both methods are combined to get more accurate in extracting keywords from an article. This combination is implemented using python programming language and tested using several articles taken randomly from the internet. The research consists of several processes namely, theoretical analysis, design, implementation, and testing. The program was created in the form of a keyword extraction website and tested from the validity of the resulting keywords. An obtained result from the whole process of the research, that is the application could produce some proper keywords in the form of phrases and words. It is better than the keywords that resulted with one method either word frequency or RAKE.
Multi-class Target Tracking Using the Semantic PHD Filter

In order for a mobile robot to be able to effectively operate in complex, dynamic environments it must be capable of understanding both where and what the objects around them are. In this paper we introduce the semantic probability hypothesis density (SPHD) filter, which allows robots to simultaneously track multiple classes of targets despite measurement uncertainty, including false positive detections, false negative detections, measurement noise, and target misclassification. The SPHD filter is capable of incorporating a different motion model for each type of target and of functioning in situations where the number of targets is unknown and time-varying. We demonstrate the efficacy of the SPHD filter via simulations with multiple target types containing both static and dynamic targets. We show that the SPHD filter performs better than a collection of PHD filters running in parallel, one for each target class.