Merging Technique Research Articles

Detecting semantic conflicts with unit tests

While modern merge techniques, such as 3-way and structured merge, can resolve textual conflicts automatically, they fail when the conflict arises not at the syntactic, but at the semantic level. Detecting such semantic conflicts requires understanding the behavior of the software, which is beyond the capabilities of most existing merge tools. Although semantic merge tools have been proposed, they are usually based on heavyweight static analyses, or need explicit specifications of program behavior. In this work, we take a different route and propose SAM (SemAntic Merge), a semantic merge tool based on the automated generation of unit tests that are used as partial specifications of the changes to be merged, and that drive the detection of unwanted behavior changes (conflicts) when merging software. To evaluate SAM’s feasibility for detecting conflicts, we perform an empirical study relying on a dataset of more than 80 pairs of changes integrated to common class elements (constructors, methods, and fields) from 51 merge scenarios. We also assess how the four unit test generation tools used by SAM individually contribute to conflict identification. Our results show that SAM performs best when combining only the tests generated by Differential EvoSuite and EvoSuite, and using our proposed testability transformations (nine detected conflicts out of 29). These results reinforce previous findings about the potential of using test-case generation to detect conflicts as a method that is versatile and requires only limited deployment effort in practice.

Suppression of radial motion in translated field-reversed configuration plasma via global gyro-effect

Abstract The radial motion of a field-reversed configuration (FRC) during translation at supersonic/Alfvénic speed was effectively suppressed by the global gyro-effect induced by toroidal flow. In magnetohydrodynamic approximation, an FRC is inherently unstable against radial motions such as tilt and wobble. Surprisingly, the magnetic structure of the FRC remained intact even during translation at speeds exceeding the ion sonic and Alfvén speeds, despite the presence of radial motions. Optical observations were conducted on the radial motion and toroidal flow of translated FRCs under various electric boundary conditions at the device end. The implementation of end-shorting led to an increased toroidal flow rate, consequently reducing the radial displacement of the FRC during translation. This control of toroidal flow from the device’s end not only suppressed radial motion but also facilitated head-on collisions in the formation of FRCs by the collisional merging technique. The findings emphasize the significance of toroidal flow control in preserving the integrity of FRC’s simply connected geometry and enhancing its formation performance.

Dual-aspect attention spatial-spectral transformer and hyperspectral imaging: A novel approach to detecting Aspergillus flavus contamination in peanut kernels

In this study, an innovative dual-aspect attention spatial-spectral transformer (DAASST) was introduced to advance postharvest quality control by the detection of Aspergillus flavus contamination and the accurate identification of contamination times in peanut kernels. The critical importance of maintaining postharvest quality and safety in nuts was recognized, with hyperspectral imaging technology being leveraged due to its great potential in non-destructive testing and quality assessment of nuts. At the heart of DAASST's innovation, an enhanced transformer architecture that incorporated an attention fusion mechanism was employed for the effective integration of the extracted features. This sophisticated integration not only improved the model’s performance but also was significantly surpassed by the capabilities of traditional machine learning methods in the context of postharvest biology and technology. Exceptional accuracy was demonstrated in testing, with 99.40% achieved in detecting Aspergillus flavus contamination and a remarkable 100% in distinguishing between different contamination times. Significant contributions to the field of postharvest biology and technology were made by merging cutting-edge feature extraction techniques, attention mechanisms, and transformer architecture to refine hyperspectral image analysis for postharvest quality control. The proven effectiveness of the DAASST in accurately detecting Aspergillus flavus and determining contamination times in peanut kernels highlighted its potential as a valuable tool for ensuring the safety and quality of postharvest nuts.

MixMatch: Flow Matching for Mixnet Traffic

Mixnets provide communication anonymity against network adversaries by routing packets independently via multiple hops, delaying them artificially at each hop, and introducing cover traffic. We show that these features (particularly the use of cover traffic) significantly diminish the effectiveness of state-of-the-art flow correlation techniques developed to link the two ends of a Tor connection. In this work, we propose novel methods to determine whether a set of endpoints exchanges packets via a mixnet and demonstrate their effectiveness by applying them to the Nym mixnet. We consider Nym in both an idealized lab setup and the official live network, and propose and compare three classifiers to conduct flow matching on it. Our statistical classifier tests whether egress packet timestamps are consistent with ingress timestamps and the (known) routing delay characteristic of the mixnet. In contrast, our two deep learning (DL) classifiers learn to distinguish matched from unmatched flow pairs from collected datasets directly, rather than relying on priors that describe the delay distribution. All three classifiers use our flow merging technique, which enables testing a match for sets of communicating endpoints of any cardinality. Considering a use case where two observed endpoints communicate exclusively to exchange a file through Nym, we find that flow matching is fast and accurate in the idealized lab setup. If flow pairs are aligned using all network observations in a download, we achieve a TPR of circa 0.6 (DL) and 0.47 (statistical) at an FPR of 10^-2 after only processing 100 observations. We evaluate classifier performance under key variations of this setup: the absence of loop cover traffic, an increased or decreased average per-mix delay, larger communicating sets (three endpoints) with faster responders, and the presence of realistic network effects (live network). The classifiers' matching performance diminishes on the live network where packet losses and variable propagation delays exist, reducing DL TPR to circa 0.26 and statistical TPR to circa 0.28 at an FPR of 10^-2. Informed by the insights of our analyses, we outline countermeasures that can be deployed in mixnets such as Nym to mitigate flow matching threats.

Enhancing Al-Quran Reading Proficiency in Higher Education: The implementation of the Focused Mad & Idgham Technique

The issue of students' weakness in Quranic reading has long been a concern in Islamic education, and it continues to persist. This study aims to innovate Quranic teaching techniques by focusing on Mad (elongation) and Idgham (merging) rules. The study examines students' motivation and involvement in Quranic reading activities and investigates their learning experiences. Twenty-nine Year 1 students and 22 students from the Islamic Education Programme, performing at medium and low levels, were selected to participate in the intervention. Data were collected and analysed by observing the students' Quranic recitations and reactions. The researcher introduced the Focused Mad (elongation) and Idgham (merging) technique (FMI) as a solution for improving Quranic reading. The findings revealed that some students in the Islamic education program still read the Quran at a less than satisfactory level. However, it was observed that students could read the Quran more effectively using the FMI technique as an intervention. The findings also indicated that the FMI technique can increase the motivation and involvement of learners in reading the Quran. Additionally, the positive perception of the FMI technique among students suggests its potential to enhance their learning experience in Quranic education, fostering engagement, comprehension, and confidence in Quranic recitation. Overall, the Focused Mad (elongation) and Idgham (merging) Technique (FMI) not only improves the ability to read the Quran but also boosts students' confidence and involvement in learning the Quran.

Innovations of AI-Generated Content (AIGC) in Stage Art: Exploring from Theory to Practice

The integration of Artificial Intelligence-Generated Content (AIGC) within stage art heralds a new era of creative possibilities, merging traditional performance techniques with cutting-edge technology. This paper explores the implications, challenges, and prospects of incorporating AIGC into stage productions, drawing on insights from industry professionals and audience feedback. Through mixed-method research involving interviews and surveys, we uncover the diverse ways in which AIGC is reshaping creative processes, enhancing audience engagement, and redefining the boundaries of stage art. Our findings reveal a complex landscape where technological innovation coexists with concerns about authenticity and artistic integrity. By delving into the nuances of this integration, we aim to provide a balanced perspective on the potential of AIGC to enrich stage art while maintaining its human essence. This study contributes to the ongoing discourse on the role of technology in the arts and offers a foundation for future research and practical applications in the field.

A Comparative Analysis of Combination of CNN-Based Models with Ensemble Learning on Imbalanced Data

This study investigates the usefulness of the Synthetic Minority Oversampling Technique (SMOTE) in conjunction with convolutional neural network (CNN) models, which include both single and ensemble classifiers. The objective of this research is to handle the difficulty of multi-class imbalanced image classification. The application of SMOTE in imbalanced picture datasets is still underexplored, even though CNNs have been shown to be successful in image classification and that ensemble learning approaches have improved their performance. To investigate whether or not SMOTE can increase classification accuracy and other performance measures when combined with CNN-based classifiers, our research makes use of a CIFAR-10 dataset that has been artificially step-imbalanced and has varying imbalanced ratios. We conducted experiments using five distinct models, namely AdaBoost, XGBoost, standalone CNN, CNN-AdaBoost, and CNN-XGBoost, on datasets that were either imbalanced or SMOTE-balanced. Metrics such as accuracy, precision, recall, F1-score, and the area under the receiver operating characteristic curve (AUC) were included in the evaluation process. The findings indicate that SMOTE dramatically improves the accuracy of minority classes, and that the combination of ensemble classifiers with CNNs and oversampling techniques significantly improves overall classification performance, particularly in situations when there is a high-class imbalance. When it comes to enhancing imbalanced classification tasks, this study demonstrates the potential of merging oversampling techniques with CNN-based ensemble classifiers to minimize the impacts of class imbalance in picture datasets. This suggests a promising direction for future research in this area.

MSCS-ICP: point cloud registration method using multi-view spatial coordinate system–ICP

The effectiveness of point cloud registration critically determines three-dimensional (3D) reconstruction accuracy involving multi-view sensors. We introduce a multi-view point cloud registration method based on multi-view spatial coordinate system–ICP to solve the problem of 3D point cloud registration from different viewpoints. By integrating a spatial rotation axis line, our method successfully establishes the spatial coordinate system tailored for multi-view sensors, ensuring that 3D point clouds derived from various perspectives are optimally positioned initially. We employ the ICP technique for point cloud merging, facilitating a seamless transition from coarse to refined registration of these multi-view 3D point clouds. During the process of spatial rotation axis line fitting, we present a Ransac-based algorithm tailored for axis line fitting that effectively removes outliers, thus significantly improving the fitting precision. Experimental results from a standard sphere reconstruction reveal that within a measurement scope of 1.3–1.9 m, our proposed method boasts a maximum error of just 0.069 mm, an average absolute error of 0.039 mm, and a root mean square error of 0.043 mm. The speed of our point cloud registration outpaces that of alternative methods. Our method notably elevates the precision and velocity of 3D point cloud registration across diverse views, demonstrating commendable adaptability and resilience.

Mining Top-k High On-shelf Utility Itemsets Using Novel Threshold Raising Strategies

High utility itemsets (HUIs) mining is an emerging area of data mining which discovers sets of items generating a high profit from transactional datasets. In recent years, several algorithms have been proposed for this task. However, most of them do not consider the on-shelf time period of items and negative utility of items. High on-shelf utility itemset (HOUIs) mining is more difficult than traditional HUIs mining because it deals with on-shelf-based time period and negative utility of items. Moreover, most algorithms need minimum utility threshold ( min_util ) to find rules. However, specifying the appropriate min_util threshold is a difficult problem for users. A smaller min_util threshold may generate too many rules and a higher one may generate a few rules, which can degrade performance. To address these issues, a novel top-k HOUIs mining algorithm named TKOS ( T op- K high O n- S helf utility itemsets miner) is proposed which considers on-shelf time period and negative utility. TKOS presents a novel branch and bound-based strategy to raise the internal min_util threshold efficiently. It also presents two pruning strategies to speed up the mining process. In order to reduce the dataset scanning cost, we utilize transaction merging and dataset projection techniques. Extensive experiments have been conducted on real and synthetic datasets having various characteristics. Experimental results show that the proposed algorithm outperforms the state-of-the-art algorithms. The proposed algorithm is up to 42 times faster and uses up-to 19 times less memory compared to the state-of-the-art KOSHU. Moreover, the proposed algorithm has excellent scalability in terms of time periods and the number of transactions.

Innovative Diagnostic Tool: Convolutional Neural Network for Early Fat Malabsorption Detection in Pediatric Patients with Chronic Diarrhea

Background: Chronic diarrhea in children poses a significant clinical challenge and can lead to adverse health outcomes. Among various causes, fat malabsorption is particularly concerning, as it may lead to inadequate nutrient absorption, malnutrition, and impaired growth. Prompt and precise diagnosis is crucial for implementing effective treatments. Objectives: The goal of this study is to utilize deep learning to create a superior diagnostic tool that exceeds traditional methods, facilitating the early identification of fat malabsorption in children suffering from chronic diarrhea. Methods: In a preliminary study involving 100 pediatric patients, 25 machine learning algorithms were evaluated. The convolutional neural network (CNN) was identified as the most effective and subsequently refined through hyperparameter tuning. Results: The CNN model exhibited exceptional performance, attaining a test accuracy of 97% and an area under the curve (AUC) score of 99.4%. These results underscore its reliability in accurately identifying cases of fat malabsorption. Conclusions: This research represents noteworthy progress in pediatric gastroenterology, merging deep learning techniques with medical expertise to develop a dependable and rapid diagnostic tool. This innovative method promises significant improvements in detecting fat malabsorption, potentially transforming clinical practices and enhancing patient outcomes in children with chronic diarrhea.

Identifying longevity profiles from longitudinal data through factor analysis and biclustering

Characterizing longevity profiles from longitudinal studies is a task with many challenges. Firstly, the longitudinal databases usually have high dimensionality, and the similarities between long-lived and non-long-lived records are a highly burdening task for profile characterization. Addressing these issues, in this work, we use data from the English Longitudinal Study of Ageing (ELSA-UK) to characterize longevity profiles through data mining. We propose a method for feature engineering for reducing data dimensionality through merging techniques, factor analysis and biclustering. We apply biclustering to select relevant features discriminating both profiles. Two classification models, one based on a decision tree and the other on a random forest, are built from the preprocessed dataset. Experiments show that our methodology can successfully discriminate longevity profiles. We identify insights into features contributing to individuals being long-lived or non-long-lived. According to the results presented by both models, the main factor that impacts longevity is related to the correlations between the economic situation and the mobility of the elderly. We suggest that this methodology can be applied to identify longevity profiles from other longitudinal studies since that factor is deemed relevant for profile classification.

Global–local feature learning for fine-grained food classification based on Swin Transformer

Separable object parts, such as the head and tail in a bird, are vital for fine-grained visual classifications. For those objects without separable parts, the classification task relies only on local and global textural image features. Although the Swin Transformer architecture was proposed to efficiently capture both local and global visual features, it still exhibits a bias towards global features. Therefore, our goal is to enhance the local feature learning capability of the Swin Transformer by adding four new modules of the Local Feature Extraction Network (L-FEN), Convolution Patch-Merging (CP), Multi-Path (MP), and Multi-View (MV). The L-FEN enhances the Swin transformer with the improved local feature capture. The CP is a localized and hierarchical adaptation of the Swin’s Patch Merging technique. The MP method integrates features across various Swin stages to accentuate local details. Meanwhile, the MV Swin transformer block supersedes traditional Swin blocks with those incorporating varied receptive fields, ensuring a broader scope of local feature capture. Our enhanced architecture, named Global–Local Swin Transformer (GL-Swin), is applied to solve a fine-grained food classification task. On three major food datasets: ISIA Food-500 UEC Food-256, and Food-101, our GL-Swin achieved accuracies of 66.75%, 85.78%, and 92.93% respectively, consistently outperforming other leading methods.

Evaluating the Knowledge for Integrating RM and VM Using BIM in the Iraqi Construction Sector

The construction industry plays a crucial role in the countries' economy, especially in the developed country. This point encourages the concerned institution to use new techniques and integrate many techniques and methods to maximize the benefits. The main objective of this research is to evaluate the use of risk management, value management, and building information modeling in the Iraqi construction industry. The evaluation process aims at two objectives. The direct objective was to evaluate the knowledge in risk management (RM), value management (VM), and building information modeling (BIM). The indirect objective was to support the participants with information related to the main items mentioned. The questionnaire survey was used as a data collection method. The participants in the survey were engineers from the Iraqi construction sector. The sample size was 121 engineers from 34 institutions with multidisciplinary backgrounds. The results showed that the engineers need to develop the training system and merge many methods and techniques in their institution to maximize the benefits using an integration model capable of dealing with the variable environment in construction, especially in developing countries like Iraq.

Review of Recent Developments in Hydrologic Forecast Merging Techniques

Accurate forecasting in hydrologic modeling is crucial for sustainable water resource management across various sectors, where predicting extreme flow phases holds particular significance due to their severe impact on the territory. Due to the inherent uncertainties in hydrologic forecasting, relying solely on a single rainfall–runoff model may not provide reliable predictions. To address this challenge, over the years, researchers have developed and applied hydrologic forecast merging (HFM) techniques that combine multiple models or ensembles to enhance forecast accuracy, reduce uncertainty, and increase confidence in the forecast. This review summarizes the progress in HFM techniques since the early 1990s and covers developments and applications in flow simulation, uncertainty analysis, monthly and seasonal streamflow predictions, ensemble forecasts, flood forecasting, and climate change analysis. The findings indicate that while HFM techniques outperform individual models regarding forecasting efficiency, their performance across applications is not uniform. Among the different methods, Bayesian model averaging (BMA) is the most popular due to its ability to reduce uncertainty and provide accurate and reliable forecasts in deterministic and probabilistic simulations. With their application simplicity, regression techniques are also robust and efficient as they perform competitively well across different model-merging applications. While specific techniques, such as model-dependent weighted averaging and neural network methods, effectively reduce forecast uncertainty, there is still room for improving forecast accuracy across different lead times. Future research can focus on advanced HFM techniques for estimating optimal weights in time-varying domains and overcoming limitations, like simulating low flows in seasonally dry catchments.

Evaluasi Pembelajaran Nontes Dalam Mata Pelajaran Akhlak di SMA MUH 1 Karanganyar

This research aims to determine the implementation of non-test evaluation on Akhlak learning at SMA MUH 1 Karanganyar. The method used is a qualitative method (qualitative description) with the aim of describing, analyzing phenomena, events and evaluating Aqidah Moral learning activities carried out by teachers for class 11 students at SMA MUH 1 Karanganyar. The data collection techniques used were observation, interviews with teachers, interviews with school principals, documentation, field notes, and triangulation. Triangulation is a data collection technique that combines several existing data merging techniques data source. The results of the research show that in general the quality of Akhlak learning at MUH 1 Karanganyar Vocational School has been running well. Likewise, the learning outcomes are also good results. However, in implementing this learning there are also things that need to be done aimed at improving learning programs.

Towards building knowledge by merging multiple ontologies with CoMerger: A partitioning-based approach

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.

Predicting merge conflicts considering social and technical assets

Concurrent contributions to a code base may introduce merge conflicts. Whereas merge conflicts are easy and common to introduce, resolving them is a difficult, time-consuming, and often error-prone task. Previous research concentrated on the emergence of merge conflicts considering technical assets in their analyses and often ignored the social perspective (e.g., developer roles). Our goal is to understand and predict merge conflicts considering social and technical assets. We devise three models for predicting merge conflicts based on common measures used by developers. The first model focuses on the social assets, the second on technical assets, and the third on technical and social assets. To evaluate our predictors, we report on a large-scale empirical study analyzing the histories of 66 real-world software systems. Specifically, we categorize developers into top or occasional contributors at project and merge-scenario level. We found that top contributors at project level and occasional contributors at merge-scenario level cause more merge conflicts than the other roles. Hence, the coordination of top contributors at project level and occasional contributors at merge-scenario level is a good starting point to minimize the occurrence of merge conflicts (especially because when these two developers work on the source branch, the chances of merge conflicts are 32.31%). Overall, we show that predicting merge conflicts incorporating developer roles is possible in practice with high accuracy (0.92) and recall (1.00) when combining technical and social assets, which is vital information to guide improvements on speculative merging techniques.

A new approach to ensemble precipitation Estimation: Coupling satellite hydrological products with backward water balance models in Large-Scale

The estimation accuracy of global precipitation datasets has been enhanced using a committee of various sources. However, the existing merging methods mainly rely on gauge-based measurements, which may have significant uncertainty across data-scarce regions. The unique approach of Triple Collocation (TC) has been widely used in various geographical information systems since it allows quantifying the uncertainties of three datasets with independent errors. Recent studies have confirmed the use of the TC for consolidating disparate triple datasets. However, merging techniques based on Modified Collocation (MC), which can combine more than three datasets, have not yet been applied to precipitation data. This study develops the backward water balance as a new approach to ensemble precipitation estimation via large-scale hydrological products without access to ground information. The proposed methodology is validated using monthly observations from more than 580 rain gauge stations in the Central Plateau watershed of Iran. In the proposed method, the error of each water balance component is statistically estimated using different methods, like MC. Furthermore, the residuals of the water balance were minimized using three techniques with different complexity levels, such as the Constrained Kalman Filter, to enforce water balance closure; hence, the error is smoothed. The precipitation products are optimally merged by employing various methods in the ensemble precipitation estimation framework. As stated in the metrics, the best-recorded estimated ensemble precipitation data improved the accuracy of the used raw precipitation products over the study region compared with the benchmark values in 10.8%, 2.3%, and 0.2% greater KGE, NSE, and TS values. Additionally, this data resulted in a 31.2% smaller RMSE. The proposed modification in the CKF method (CKF2) showed better results to close the water balance.

Necessity of artificial intelligence techniques for power quality issues in electric vehicles

<p>World transportation is in an electrified mass transitional phase due to the predominant features such as a greener environment, fewer maintenance requirements, improved reliability and energy management efficiency compared to conventional transportation. Numerous difficulties have emerged since electric vehicle sales hit record highs, including how electrified transportation works with the grid. Emerging innovations and technological advances in the field of artificial intelligence have led to the development of enhanced controllers for electric vehicle penetration with the grid. This work includes information on the integration of artificial intelligence strategies used for the mitigation of power quality issues. By merging artificial intelligence techniques into the grid, users can achieve efficient energy use and beneficial power interactions between the grid and electric vehicles. To assess the network's practicality, this research develops a charging model that uses a Tasmanian Devil optimization (TDO) algorithm based on MATLAB software. According to research, TDO improves the waveform quality by reducing the THD with better performance and achieves efficient power transfer between the grid and EVs.</p>

DIY Your EasyNAS for Vision: Convolution Operation Merging, Map Channel Reducing, and Search Space to Supernet Conversion Tooling.

Despite its popularity as a one-shot Neural Architecture Search (NAS) approach, the applicability of differentiable architecture search (DARTS) on complex vision tasks is still limited by the high computation and memory costs incurred by the over-parameterized supernet. We propose a new architecture search method called EasyNAS, whose memory and computational efficiency is achieved via our devised operator merging technique which shares and merges the weights of candidate convolution operations into a single convolution, and a dynamic channel refinement strategy. We also introduce a configurable search space-to-supernet conversion tool, leveraging the concept of atomic search components, to enable its application from classification to more complex vision tasks: detection and semantic segmentation. In classification, EasyNAS achieves state-of-the-art performance on the NAS-Bench-201 benchmark, attaining an impressive 76.2% accuracy on ImageNet. For detection, it achieves a mean average precision (mAP) of 40.1 with 120 frames per second (FPS) on MS-COCO test-dev. Additionally, we transfer the discovered architecture to the rotation detection task, where EasyNAS achieves a remarkable 77.05 mAP 50 on the DOTA-v1.0 test set, using only 21.1 M parameters. In semantic segmentation, it achieves a competitive mean intersection over union (mIoU) of 72.6% at 173 FPS on Cityscape, after searching for only 0.7 GPU-day.