Reviewerly/exHarmony huggingface dataset:
A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer

In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in estimating ilmenite content. Heavy mineral analysis indicated that valuable minerals in the placer sand are zircon, ilmenite, leucoxene, rutile, anatase, and monazite. In this study, five materials, namely rutile, anatase, leucoxene, zircon, and monazite, were used as the input variables to estimate ilmenite content based on the above mentioned soft computing models. Of the whole dataset, 325 samples were used to build the regarded soft computing models; 80 remaining samples were used for the models’ verification. Root-mean-squared error (RMSE), determination coefficient (R2), a simple ranking method, and residuals analysis technique were used as the statistical criteria for assessing the model performances. The numerical experiments revealed that soft computing techniques are capable of estimating the content of ilmenite with high accuracy. The residuals analysis also indicated that the SGB model was the most suitable for determining the ilmenite content in the context of this research.
Understanding predictive factors for merge conflicts

Context: Merge conflicts often occur when developers change the same code artifacts. Such conflicts might be frequent in practice, and resolving them might be costly and is an error-prone activity. Objective: To minimize these problems by reducing merge conflicts, it is important to better understand how conflict occurrence is affected by technical and organizational factors. Method: With that aim, we investigate seven factors related to modularity, size, and timing of developers contributions. To do so, we reproduce and analyze 73504 merge scenarios in GitHub repositories of Ruby and Python MVC projects. Results: We find evidence that the likelihood of merge conflict occurrence significantly increases when contributions to be merged are not modular in the sense that they involve files from the same MVC slice (related model, view, and controller files). We also find bigger contributions involving more developers, commits, and changed files are more likely associated with merge conflicts. Regarding the timing factors, we observe contributions developed over longer periods of time are more likely associated with conflicts. No evaluated factor shows predictive power concerning both the number of merge conflicts and the number of files with conflicts. Conclusion: Our results could be used to derive recommendations for development teams and merge conflict prediction models. Project management and assistive tools could benefit from these models.
A Novel Evaluation Model for a Mixed-Reality Surgical Navigation System: Where Microsoft HoloLens Meets the Operating Room

HoloLens-based mixed-reality surgical navigation system (MR-SNS) technology has made great progress. However, the methodology for evaluating users' perceptions concerning the safety, comfort, and efficiency of MR-SNS is still in its infancy. This study was intended to develop a method to systematically evaluate an existing MR-SNS system during actual clinical applications. This method differs from other existing methods currently used in industry, education, and device maintenance. Based on analytical hierarchy process theory and ergonomics evaluation methods, in this article, we propose a novel multicriteria evaluation model for a HoloLens-based MR-SNS. The model includes factors such as comfort, safety, and effectiveness, and is performed in an actual clinical application. A comprehensive experimental platform and scoring system that can analyze all indicators was built. The validation test showed no statistically significant differences in the accuracy of the 3 different movement patterns (P = .95, P > .05). However, the static pattern showed the best accuracy. In addition, no significant difference (P = .68, P > .05) in accuracy was found under 4 kinds of illuminance. A comparison of the results of this evaluation model and the input from experts who use the HoloLens-based MR-SNS in hospitals, indicated that this model has good precision (100%), recall (80%), and F1-measure (88.89%). The results highlighted the full efficacy of the proposed model in determining whether this system can be used in clinical trials to provide indicators for preliminary ex ante feasibility studies. This article describes the lessons learned from conducting this evaluation study of MR-SNS as part of the design process.
ObserVAR: Visualization System for Observing Virtual Reality Users using Augmented Reality

While virtual reality (VR) tools provide an immersive learning experience for students, it is difficult for an instructor to observe the students' learning activities in a virtual environment (VE). Thus, it hinders interactions that could occur between the instructor and students, which are usually required in a classroom environment to understand how each student learns. Previous work has added virtual awareness cues that can help a small group of students to collaborate in a VE. However, when the number of students increases, such virtual awareness cues can cause visual clutter and confuse the instructor. We propose ObserVAR, a visualization system that allows the instructor to observe students in a VE at scale. ObserVAR uses augmented reality techniques to visualize each student's gaze in a VE and improves the instructor's awareness of the entire class. The visualizations are then optimized to reduce visual clutter in the scene using a force-directed graph drawing algorithm. In designing ObserVAR, we first investigated visualizations that can provide the instructor with an overall awareness of the VE that can be scaled up as the number of users increases. Second, we optimized the visualization of students by leveraging a graph drawing algorithm to reduce the visual clutter in the class scene. We compared the performance of our prototype with some commercially available user interfaces for VE classrooms. In our study, ObserVAR has demonstrated improvement and flexibility in several application scenarios.
Hand Gesture Recognition Using PCA Based Deep CNN Reduced Features and SVM Classifier

Automatic recognition of vision based static hand gesture images is a challenging task due to illumination changes, diversity in user hand shape and high inter class similarities. This paper proposes novel techniques to develop a user independent hand gesture recognition system, considering the above challenges. First, the gesture recognition performance is analyzed using proposed pre-trained AlexNet features. In this proposition the deep features are extracted from fully connected (FC) layers such as 'FC6 and 'FC7' of pre-trained AlexNet. A support vector machine (SVM) based classifier with linear kernel is used to classify gesture poses. The highest recognition accuracy is evaluated using the deep feature extracted from 'FC6 and 'FC7' independently and combination of both the feature vector with SVM classifier. Second, feature dimension of deep features are reduced using principal component analysis (PCA) based dimension reduction technique for further improvement in gesture recognition accuracy. The performance of the proposed technique is evaluated using leave-one-subject-out cross validation (LOO CV) and holdout CV test. The extensive analysis is performed on 36 American Sign Language (ASL) benchmark static hand gesture dataset using both the CV test. The experimental result shows that, the proposed technique is superior as compared to state-of-the-art techniques.
Study on Power Line Insulator Defect Detection via Improved Faster Region-Based Convolutional Neural Network

Machine learning based on hand-crafted features from the original aerial insulator image has shown promising results in the insulator defect detection in recent years. Such methodologies, nevertheless, are not suitable for detecting insulator defect in complex backgrounds, which intrinsically relies on sufficient prior knowledge, low background interference, and certain object scales. Therefore, in this study, an effective insulator defect detection model using improved Faster Region-based Convolutional neural network (Faster R-CNN) based on the original aerial insulator image is proposed. The soft non-maximum suppression (Soft-NMS) is utilized to improve the performance of detection overlap insulator and ResNet 101 model is adopted to effectively extract feature from insulator images. Furthermore, the proposed method has more accurate target location and higher average accuracy by comparing traditional Faster R-CNN.
Identifying self-admitted technical debt through code comment analysis with a contextualized vocabulary

Abstract Context Previous work has shown that one can explore code comments to detect Self-Admitted Technical Debt (SATD) using a contextualized vocabulary. However, current detection strategies still return a large number of false positives items. Moreover, those strategies do not allow the automatic identification of the type of debt of the identified items. Objective This work applies, evaluates, and improves a set of contextualized patterns we built to detect self-admitted technical debt using code comment analysis. We refer to this set of patterns as the self-admitted technical debt identification vocabulary. Method We carry out three empirical studies. Firstly, 23 participants analyze the patterns of a previously defined contextualized vocabulary and register their level of importance in identifying SATD items. Secondly, we perform a qualitative analysis to investigate the relation between each pattern and types of debt. Finally, we perform a feasibility study using a new vocabulary, improved based on the results of the previous empirical studies, to automatically identify self-admitted technical debt items, and types of debt, that exist in three open source projects. Results More than half of the new patterns were considered decisive or very decisive to detect technical debt items. The new vocabulary was able to find items associated to code, design, defect, documentation, and requirement debt. Thus, the result of the work is an improved vocabulary that considers the level of importance of each pattern and the relationship between patterns and debt types to support the identification and classification of SATD items. Conclusion The studies allowed us to improve a vocabulary to identify self-admitted technical debt items through code comments analysis. The results show that the use of pattern-based code comment analysis can contribute to improve existing methods, or create new ones, for automatically identifying and classifying technical debt items.
Syntactic Complexity in Adapted Teaching Materials: Differences Among Grade Levels and Implications for Benchmarking

Abstract An extensive body of research has investigated the role of syntactic complexity in gauging the linguistic complexity of reading texts, particularly for the purpose of determining their grade appropriateness. However, little such research has focused on adapted teaching materials for English as a foreign language (EFL) contexts, and to date there has been no systematic effort in establishing syntactic complexity benchmarks to guide text adaptation practices in such contexts. This paper reports on a large‐scale study that assessed the quantitative differences in syntactic complexity among adapted teaching materials for different grade levels in the EFL curricula in China. Our data consisted of 3,368 adapted English texts solicited from a corpus of teaching materials approved for use in the 12 primary and secondary grade levels in China by the Chinese Ministry of Education. All texts were analyzed using 8 syntactic complexity measures representing different dimensions of syntactic complexity. All 8 measures showed significant between‐level differences with moderate to large effect sizes and nonuniform patterns of progression, and 5 measures were identified as significant predictors of grade levels in a logistic regression analysis. The implications of our results for establishing syntactic complexity benchmarks to inform future text adaptation practices are discussed.
Simple and Efficient Pattern Matching Algorithms for Biological Sequences

The remarkable growth of biological data is a motivation to accelerate the discovery of solutions in many domains of computational bioinformatics. In different phases of the computational pipelines, pattern matching is a very practical operation. For example, pattern matching enables users to find the locations of particular DNA subsequences in a database or DNA sequence. Furthermore, in these expanding biological databases, some patterns are updated over time. To perform faster searches, high-speed pattern matching algorithms are needed. The present paper introduces three pattern matching algorithms that are specially formulated to speed up searches on large DNA sequences. The proposed algorithms raise performance by utilizing word processing (in place of the character processing presented in previous works) and also by searching the least frequent word of the pattern in the sequence. In terms of time cost, the experimental results demonstrate the superiority of the presented algorithms over the other simulated algorithms.
Optimizing segmentation granularity for neural machine translation

In neural machine translation (NMT), it has become standard to translate using subword units to allow for an open vocabulary and improve accuracy on infrequent words. Byte-pair encoding (BPE) and its variants are the predominant approach to generating these subwords, as they are unsupervised, resource-free, and empirically effective. However, the granularity of these subword units is a hyperparameter to be tuned for each language and task, using methods such as grid search. Tuning may be done inexhaustively or skipped entirely due to resource constraints, leading to sub-optimal performance. In this paper, we propose a method to automatically tune this parameter using only one training pass. We incrementally introduce new BPE vocabulary online based on the held-out validation loss, beginning with smaller, general subwords and adding larger, more specific units over the course of training. Our method matches the results found with grid search, optimizing segmentation granularity while significantly reducing overall training time. We also show benefits in training efficiency and performance improvements for rare words due to the way embeddings for larger units are incrementally constructed by combining those from smaller units.
Boxy Vehicle Detection in Large Images

Camera-based object detection and automated driving in general have greatly improved over the last few years. Parts of these improvements can be attributed to public datasets which allow researchers around the world to work with data that would often be too expensive to collect and annotate for individual teams. Current vehicle detection datasets and approaches often focus on axis-aligned bounding boxes or semantic segmentation. Axis-aligned bounding boxes often misrepresent vehicle sizes and may intrude into neighboring lanes. While pixel level segmentations are more accurate, they can be hard to process and leverage for trajectory planning systems. We therefore present the Boxy dataset for image-based vehicle detection. Boxy is one of the largest public vehicle detection datasets with 1.99 million annotated vehicles in 200,000 images, including sunny, rainy, and nighttime driving. If possible, vehicle annotations are split into their visible sides to give the impression of 3D boxes for a more accurate representation with little overhead. Five megapixel images with annotations down to a few pixels make this dataset especially challenging. With Boxy, we provide initial benchmark challenges for bounding box, polygon, and real-time detections. All benchmarks are open-source so that additional metrics and benchmarks may be added at https://boxy-dataset.com.
Adversarial Learning-Based Semantic Correlation Representation for Cross-Modal Retrieval

Cross-modal retrieval has become a hot issue in past years. Many existing works pay attentions on correlation learning to generate a common subspace for cross-modal correlation measurement, and others use adversarial learning technique to abate the heterogeneity of multimodal data. However, very few works combine correlation learning and adversarial learning to bridge the intermodal semantic gap and diminish cross-modal heterogeneity. This article proposes a novel cross-modal retrieval method, named Adversarial Learning based Semantic COrrelation Representation (ALSCOR), which is an end-to-end framework to integrate cross-modal representation learning, correlation learning, and adversarial. Canonical correlation analysis model, combined with VisNet and TxtNet, is proposed to capture cross-modal nonlinear correlation. Besides, intramodal classifier and modality classifier are used to learn intramodal discrimination and minimize the intermodal heterogeneity. Comprehensive experiments are conducted on three benchmark datasets. The results demonstrate that the proposed ALSCOR has better performance than the state of the arts.
Particle Swarm Optimized Autonomous Learning Fuzzy System

The antecedent and consequent parts of a first-order evolving intelligent system (EIS) determine the validity of the learning results and overall system performance. Nonetheless, the state-of-the-art techniques mostly stress on the novelty from the system identification point of view but pay less attention to the optimality of the learned parameters. Using the recently introduced autonomous learning multiple model (ALMMo) system as the implementation basis, this article introduces a particle swarm-based approach for the EIS optimization. The proposed approach is able to simultaneously optimize the antecedent and consequent parameters of ALMMo and effectively enhance the system performance by iteratively searching for optimal solutions in the problem spaces. In addition, the proposed optimization approach does not adversely influence the "one pass" learning ability of ALMMo. Once the optimization process is complete, ALMMo can continue to learn from new data to incorporate unseen data patterns recursively without full retraining. The experimental studies with a number of real-world benchmark problems validate the proposed concept and general principles. It is also verified that the proposed optimization approach can be applied to other types of EISs with similar operating mechanisms.
Evaluating Multivariate Network Visualization Techniques Using a Validated Design and Crowdsourcing Approach

Visualizing multivariate networks is challenging because of the trade-offs necessary for effectively encoding network topology and encoding the attributes associated with nodes and edges. A large number of multivariate network visualization techniques exist, yet there is little empirical guidance on their respective strengths and weaknesses. In this paper, we describe a crowdsourced experiment, comparing node-link diagrams with on-node encoding and adjacency matrices with juxtaposed tables. We find that node-link diagrams are best suited for tasks that require close integration between the network topology and a few attributes. Adjacency matrices perform well for tasks related to clusters and when many attributes need to be considered. We also reflect on our method of using validated designs for empirically evaluating complex, interactive visualizations in a crowdsourced setting. We highlight the importance of training, compensation, and provenance tracking.
CALC2.0: Combining Appearance, Semantic and Geometric Information for Robust and Efficient Visual Loop Closure

Traditional attempts for loop closure detection typically use hand-crafted features, relying on geometric and visual information only, whereas more modern approaches tend to use semantic, appearance or geometric features extracted from deep convolutional neural networks (CNNs). While these approaches are successful in many applications, they do not utilize all of the information that a monocular image provides, and many of them, particularly the deep-learning based methods, require user-chosen thresholding to actually close loops - which may impact generality in practical applications. In this work, we address these issues by extracting all three modes of information from a custom deep CNN trained specifically for the task of place recognition. Our network is built upon a combination of a semantic segmentator, Variational Autoencoder (VAE) and triplet embedding network. The network is trained to construct a global feature space to describe both the visual appearance and semantic layout of an image. Then local keypoints are extracted from maximally-activated regions of low-level convolutional feature maps, and keypoint descriptors are extracted from these feature maps in a novel way that incorporates ideas from successful hand-crafted features. These keypoints are matched globally for loop closure candidates, and then used as a final geometric check to refute false positives. As a result, the proposed loop closure detection system requires no touchy thresholding, and is highly robust to false positives - achieving better precision-recall curves than the state-of-the-art NetVLAD, and with real-time speeds.
Comparing Convolutional Neural Network(CNN) models for machine learning-based drone and bird classification of anti-drone system

As drones become more advanced and commercialized, crimes using drones are also on rise. For this reason, development of anti-drone systems is increasing. In this paper, CNN model is examined that is suitable for visible camera-based drone identification. The CNN models used for the validation are Alexnet, GoLeNet, Inception-v3 Vg16, Resnet-18, Resnet-50 and Squezezenet. These seven models have already been validated in the ImageNet Large Scale Visual Recognition Competition (ILSVRC). In ILSVRC, 1000 labels are classified, but in this study limits them to three drones, birds and backgrounds. Therefore, it is necessary to verify whether the three labels are the same as the ILSVRC result. In order to verify this, CNN models are learned and tested in the same environment. The experimental results show that the performance of Alexnet, Resnet and Squeeznet is relatively better then the others, unlike the performance of CNN known through ILSVRC. his result shows that a shallow network with a simple structure is more reasonable when the number of labels is small. Based on these results, the further work is to develop a neural network optimized for Drone identification.