Reviewerly/exHarmony huggingface dataset:
[Spatial Distribution Characteristics and Source Analysis of Heavy Metals in Urban River Surface Sediments from the Lanzhou Reach of the Yellow River].

In order to investigate the contamination characteristics and potential sources of heavy metals from the urban river surface sediments in the Yellow River Basin, we selected the Lanzhou reach of the Yellow River as the object of investigation. A total of 46 surface sediment samples were collected along the Lanzhou reach of the Yellow River, and the contents of eight heavy metals, Cr, Ni, Cu, Zn, As, Cd, Hg, and Pb, were determined by using inductively coupled plasma mass spectrometry and an atomic fluorescence spectrometer. Contamination indexes including single factor pollution index (Pi) and geo-accumulation index (Igeo), together with the sediment pollution index (SPI), were used to assess heavy metal pollution characteristics and ecological risk levels in the urban river surface sediments of the Lanzhou reach of the Yellow River. Pearson's correlation analysis (CA), positive matrix factorization (PMF), and principal component analysis/absolute principal component score (PCA/APCS) were jointly employed to quantitatively analyze pollution sources of heavy metals. The results showed that the mean concentrations of the majority of heavy metals exceeded their corresponding background values of Gansu Province and Lanzhou City with the exception of As, and the spatial distribution of high concentrations of heavy metals was mainly concentrated in the corner of the river. Based on the single factor pollution and geo-accumulation indexes of the eight heavy metals, in the Lanzhou reach of the Yellow River, Cr was the dominant pollution element in the urban river surface sediments, followed by Cd and Ni. Additionally, the SPI values for the eight heavy metals in the surface sediments ranged from 0.48 to 8.56, presenting natural to low ecological risk level. Furthermore, source apportionment revealed that a mixture source of industrial and agricultural activities (77.6%) was the largest contributor of Cr, Ni, Zn, As, Cd, and Pb in the urban river surface sediments, followed by natural sources (11.4%) and a mixed source of industrial and traffic activities (11%).
A Novel Method Determining the Size and Angle of an Object Using a Depth Camera Without Reference

In traditional methods, a fixed object is taken as a reference for size determination. The size of the other object is calculated by comparing the dimensions of this reference object. However, when it is desired to measure objects at different heights, the measurement of the reference object must also be changed. In the study carried out, the size and angle of the products flowing through the line were determined by using a depth camera. The data set was created by taking the measurements of an object of known dimensions from 34 different distances. The mentioned data set consists of the ratio of the number of pixels of the edges of the object to the lengths of the edges. By comparing the correlation between the obtained data set and the distance values, a mathematical equation was extracted with the help of the MATLAB program. With the help of this equation, regardless of the height and color of the object, only the distance to the camera and all 3 dimensions can be calculated. In addition, the angle of the object with the software-generated reference line was calculated by taking the long side of the object as a reference. In this way, the size and angle of the products flowing through the line were determined with a single camera without the need for a reference object and without any color and size discrimination.
A comparative study of model-centric and data-centric approaches in the development of cardiovascular disease risk prediction models in the UK Biobank

Abstract Aims A diverse set of factors influence cardiovascular diseases (CVDs), but a systematic investigation of the interplay between these determinants and the contribution of each to CVD incidence prediction is largely missing from the literature. In this study, we leverage one of the most comprehensive biobanks worldwide, the UK Biobank, to investigate the contribution of different risk factor categories to more accurate incidence predictions in the overall population, by sex, different age groups, and ethnicity. Methods and results The investigated categories include the history of medical events, behavioural factors, socioeconomic factors, environmental factors, and measurements. We included data from a cohort of 405 257 participants aged 37–73 years and trained various machine learning and deep learning models on different subsets of risk factors to predict CVD incidence. Each of the models was trained on the complete set of predictors and subsets where each category was excluded. The results were benchmarked against QRISK3. The findings highlight that (i) leveraging a more comprehensive medical history substantially improves model performance. Relative to QRISK3, the best performing models improved the discrimination by 3.78% and improved precision by 1.80%. (ii) Both model- and data-centric approaches are necessary to improve predictive performance. The benefits of using a comprehensive history of diseases were far more pronounced when a neural sequence model, BEHRT, was used. This highlights the importance of the temporality of medical events that existing clinical risk models fail to capture. (iii) Besides the history of diseases, socioeconomic factors and measurements had small but significant independent contributions to the predictive performance. Conclusion These findings emphasize the need for considering broad determinants and novel modelling approaches to enhance CVD incidence prediction.
Research on the application of remote sensing and GIS technology in railroad engineering geology survey

Railroad engineering geological survey is a general and important work in the railroad design process. The effective combination of remote sensing and GIS technology has broad application prospects in railroad survey, planning and construction. In this paper, we propose an image classification algorithm in railroad engineering geological survey, and determine the best segmentation scale through several experiments. Based on the spectral and geometric information features of different lithologies, a classification rule set with the KNN and SVM methods are used for lithology classification, and the accuracy evaluation shows that the classification results are more reliable. This work will greatly improve the segmentation efficiency and make the multi-scale segmentation technology of remote sensing images truly automated.
Sensor fusion-based obstacle detection method for hazy environment

Based on camera and millimeter wave radar data fusion, an intelligent vehicle obstacle detection method suitable for hazy environment is proposed. Firstly, by taking the effectiveness of obstacle detection after image dehazing as the evaluation standard, a series of typical dehazing networks are compared and the best one was selected for image preprocessing. An obstacle detection model based on YOLOv5s depth network was established; Then, the camera data and radar data are fused in time and space, and the sensor data is associated based on the global nearest neighbor data association algorithm. Finally, the effectiveness of the proposed method is verified by open source data sets and real vehicle experiments.
Sentiment Analysis of student on Online Lectured During Covid-19 Pandemic Using K-Means and Naïve Bayes Classifier

&#x0D; &#x0D; &#x0D; &#x0D; The Covid-19 pandemic that occurred at the end of 2019 caused life changes, one of which was the learning process in universities. in accordance with the instructions issued by the Minister of Education as an effort to prevent the spread of Covid-19 by conducting online learning. Learning that is carried out online with a long period of time there are many obstacles such as networks and learning processes that are not optimal. Thus, students have mixed opinions on online lectures. Twiter is one of the social media used by students in expressing opinions on online lectures. The sentiment that users write on Twitter has not been determined in a more positive or negative direction. Sentiment analysis is needed to determine the tendency of student opinions towards online lectures. In this study, a sentiment analysis of online lectures was carried out using the K-Means and Naïve Bayes Classifier methods. The K-Means method is used to perform labeling or clustering and the Naïve Bayes Classifier is used as the classification. Based on research conducted with testing the Naïve Bayes Classifier model with a 70% division of training data and 30% test data using matrix confussion resulted in an accuracy of 95.67%.&#x0D; &#x0D; &#x0D; &#x0D;
Implementation of Naïve Bayes Method with Certainty Factor for Disease and Pest Diagnosis on Onion Plants

&#x0D; &#x0D; &#x0D; &#x0D; Shallots can be regarded as non-substituted, which is a plant that is used as a food seasoning and herbal medicine. Every year, the demand for shallots is increasing. But along with the ever-increasing demand, it is inversely proportional to the lack of availability. The cause of this is the lack of knowledge about shallot cultivation, including pest and disease disturbances. The purpose of this research is to help farmers diagnose early diseases and pests that attack shallot plants. With the presence of these pests and diseases, a system that contains knowledge from an expert is needed to diagnose early symptoms experienced by plants. In this study, the authors created an expert system for the diagnosis of diseases and pests on shallot plants. Researchers used the Naïve Bayes method as a classification method for each selected symptom. Then the Certainty Factor as a method of determining the value of confidence in the diagnosis results in the first method. In this study, it produced an accuracy rate of 97%.&#x0D; &#x0D; &#x0D; &#x0D;
Fake Twitter Followers Detection using Machine Learning Approach

Fake user accounts on social media are a serious threat. Artificial intelligence can play an important role in getting rid of fake user accounts. Twitter has a problem with fake accounts that are run by automated bots. These bots are used for bad things like spamming, sabotaging trends, and getting more followers. In this research, publicly available information about Twitter users, such as their activity patterns, profile information, and tweets, to figure out how real each one is. Twitter’s API and other ways of getting information will be used to get user data. The proposed model consists of a machine learning technique, and it detects whether the user accounts are real or fake. Various algorithms such as logistic regression, long short-term memory, K-mean, and random forest are applied to evaluate the proposed model. The experimental results show that the random forest algorithm has obtained the highest accuracy 0.7557, a precision of 0.7277, and F1 of 0.7943 among all other algorithms.
Impact of Digital Payment Systems and Blockchain on Economic Growth

Blockchain technology's emergence has the potential to significantly transform the global economy. The financial industry is currently showing a lot of interest in blockchain, a new technology. Small economies are setting the bar for how to use this digital asset and technology. More emphasis is being paid to the potential of blockchain technology to address enduring problems with economic development. Supporters of blockchain assert that it will boost opportunities for exchange and collaboration by reducing the need for intermediaries and the resistance they cause. This research is aimed to evaluate the impact of blockchain on economic growth that has a potential to the technological era. In order to evaluate this concept empirically, a quantitative analysis has done to assess the research construct. Data collected from public and domestic banks from Sharjah UAE. 209 participants screened responses included for analysis using SmartPLS 4. A composite reliability, validity, correlation and hypothesis testing performed by calculating PLS-SEM algorithm. Findings revealed as significant of each variable that represents a positive impact of digital payment on economic growth. The findings may contribute to major researcher in intended to explore financial sector such as banks, the academic individuals and scholars.
DeepScene, DeepVis, DeepDist, And DeepReject: Image-Based Visibility Estimation System For UAV

This paper presents a method of multiple Deep Learning model-based visibility estimation for a fixed and drone camera under adversarial conditions. High visibility is required for safe flights at airports, cities, and fields. On the other hand, local weather conditions can suddenly vary during the flight. Fog and clouds are known to cause invisibility that impedes critical issues for flights. Due to expensive systems, visibility measurement sensors have been used at very limited airports. For this, lower-cost methods with portable apparatus have been desired for flight routines. Therefore, this paper proposes a camera-based visibility estimation method using multiple Deep Learning models. However, even state-of-the-art models can be degraded by strong illumination like sunbeams and artificial lighting. For this issue, DeepReject is introduced to exclude input images. DeepScene is proposed to apply for panoptic segmentation of objects. In DeepDist, fog levels can be estimated by apparent changes in each geo-tagged object, i.e., vehicle, and mountain. An image-data regression-based DeepVis is also proposed to apply for non-geo-tagged scenes, i.e., sky and clouds. Using four DeepXs, experimental results show the robustness, stability, and accuracy of the proposed multiple DL models for the unmanned aerial vehicle flight conditions over a single DL.
Towards Automated Code Reviews: Does Learning Code Structure Help?

Code review is a crucial ingredient to quality software development, but requires a large amount of time and effort for developers. To optimise this manual process, recent research on automated code review seeks to leverage Neural Machine Translation (NMT) models to perform tasks such as automated code improvement. A recent work had pretrained the NMT model for automated code review in order to equip the model with general coding knowledge. However, their pretraining approach is generic to natural languages, which does not leverage the unique properties of coding languages. Therefore, we set out to explore two state-of-the-art pretrained NMT models (i.e., CodeT5 and GraphCodeBERT) that were designed to learn code structure. We studied the models’ abilities to generate correct code improvement through an empirical evaluation based on five different datasets. Our results showed that in terms of generating correct code sequences, CodeT5, GraphCodeBERT, and the prior work achieved an average accuracy of 22%, 18%, and 10%, respectively. In terms of generating correct dataflow structures, they achieved an average accuracy of 33%, 30%, and 22%, respectively. The results suggested that the code structure focused approaches could outperform the generic pretraining approach. This work contributes towards enhancing automated code review techniques by understanding the effectiveness of code structure focused NMT models.
Technical Debt Diffuseness in the Apache Ecosystem: A Differentiated Replication

Technical debt management is a critical activity that is gaining the attention of both practitioners and researchers. Several tools providing automatic support for technical debt management have been introduced over the last years. SonarQube is one of the most widely applied tools to automatically measure technical debt in software systems. SonarQube has been adopted to quantify the diffuseness of technical debt in projects of the Apache Software Foundation ecosystem. Lenarduzzi et al. [1] found that the vast majority of technical debt issues in the code are code smells and that, surprisingly, developers tend to take more time to remove severe issues than the less-severe ones. While this study provides very interesting insights both for researchers and practitioners interested in technical debt management, we identified some major limitations that could have led to results that do not perfectly reflect reality. This study aims to address such limitations by presenting a differentiated replication study. Our findings have pointed out significant differences with the reference work. The results show that technical debt issues appear much more rarely than what the reference work reported.In this study, we implemented a new methodology to calculate the diffuseness of SonarQube issues at project and commit level, based on the reconstruction of the SonarQube quality profile in order to understand how the quality profile has evolved and to compare the number of active rules per category and severity level with the respective number of issues found. The results show that over 50% of rules active in the quality profile, are Code Smell rules and that over 90% of the issues belong to Code Smell category. Furthermore, analyzing the life span of the issues, we found that developers take into account the level of severity of the issues only for the Bug category, thus fixing the issues starting from the most severe, which is not the case for the other categories.
Leveraging and Evaluating Automatic Code Summarization for JPA Program Comprehension

Accurate and up-to-date software documentation is an important factor in the maintenance and evolution of software systems. Especially with legacy software, documentation is often outdated or missing entirely and manual redocumentation is not feasible. In recent years, automatic code summaries based on artificial neural network (ANN) models have been proposed to address this problem, and metric-based evaluations suggest promising quality of the generated summaries. To evaluate the applicability of state-of-the-art code summarization in an industry context, we conduct an expert evaluation to assess the quality of the generated summaries for JPA program comprehension. We then compare the level of quality perceived by human experts for both predicted and reference summaries and discuss how these results are influenced by industry-specific requirements and how they correlate with automatically computed source code summary metrics. The results show that the quality of predicted summaries is predominantly (about 80%) poor in terms of accuracy and completeness. Moreover, the results support the generally increasing consensus that the widely used BLEU or ROUGE-L score is not a suitable means of evaluating the quality of code summarization. While these metrics are an adequate means of comparison with existing related work, they cannot reflect the human-perceived level of quality in practice.
How Robust Is a Large Pre-trained Language Model for Code Generationƒ A Case on Attacking GPT2

Large pre-trained language models have shown strong capabilities in the field of natural language processing, and in recent years research demonstrated that they can also produce surprising results from natural language descriptions in automatic code-generation applications. Although such models have performed well in a variety of domains, there is evidence that they can be affected by adversarial attacks, Consequently, it has become important to measure the robustness of models by producing adversarial examples. In this study, we proposed an attack method called the Modifier for Code Generation Attack (M-CGA), which is the first time a white box adversarial attack has been applied to the field of code generation. The M-CGA method measures the robustness of a model by producing adversarial examples that can cause the model to produce code that is incorrect or does not meet the criteria for use. Preliminary experimental results showed the M-CGA method to be an effective attack method, providing a new research direction in automatic code synthesis.
An Empirical Comparison of Model-Agnostic Techniques for Defect Prediction Models

Recently, software defect prediction studies have attempted to make black-box defect prediction models explainable and actionable. State-of-the-art defect prediction studies have utilized various model-agnostic techniques derived from the explainable AI domain as key tools to make the predictions easier for practitioners to understand. However, it has not been sufficiently investigated whether there is inconsistent information within local explanations generated by different model-agnostic techniques when interpreting a defect prediction. If local explanations generated by heterogeneous model-agnostic techniques consist of different information, the derivable insights to understand and act upon defect predictions becomes less viable and it may cause ineffective or even incorrect defect corrections. In this research, we empirically analyzed 323,844 local explanations generated by three different model-agnostic techniques: (1) LIME, (2) SHAP, and (3) BreakDown. These local explanations were analyzed in terms of how the contributions of features were distributed, how the contributions were ranked, and whether the contributions were contradictory. We concluded that (i) different model-agnostic techniques provide practitioners with local explanations where average contributions of the top-ranked features are different; (ii) different model-agnostic techniques provide practitioners with local explanations consisting of different contribution rankings and inconsistent contribution directions of top-ranked features. Therefore, we recommend that practitioners should avoid using model-agnostic techniques interchangeably and must perform a multi-faceted manual validation when planning actions based on the local explanations.
Self-Adaptive Ensemble-based Approach for Software Effort Estimation

Software Effort Estimation (SEE) is one of the most challenging tasks in software project management. In the literature, the researchers addressed the SEE problem in different ways, including models developed using machine learning (ML). Integrating individual models (Ensemble) is an active research topic in the ML domain, leading to improved performance than the individual models. Ensembling approaches such as bagging, boosting, and stacking have been extensively studied in the literature for improved SEE. The performance of an ensemble model largely depends upon tuning the hyperparameters of each learner and assigning the correct weight to each base learner. To this end, this work proposes a self-adaptive ensemble-based approach that combines hyperparameter tuning and weight assignment problems in a single step and solves them using optimization problems. The proposed model optimizes the hyperparameters of each learner using the particle swarm optimization (PSO) algorithm. Also, the weights of each base learner are optimized using Sequential Least SQuares Programming (SLSQP). An experimental analysis is conducted to evaluate the proposed model over different SEE datasets from PROMISE and ISBSG data repositories. The results show that the proposed ensemble approach performed better than the individual models. Further, we compared our proposed approach with other commonly used ensembling approaches in the literature. We found that the proposed ensemble approach provides better predictive performance than other ensembles.