Reviewerly/exHarmony huggingface dataset:
On the impact of single and co-occurrent refactorings on quality attributes in android applications

Android applications must evolve quickly to meet new user requirements, to facilitate bug fixing or to adapt to technological changes. This evolution can lead to various software quality problems that may hinder maintenance and further evolution. Code refactoring is a key practice that is employed to ensure that the intent of a code change is properly achieved without compromising internal software quality. While the impact of refactoring on software quality has been widely studied in object-oriented software in general, its impact in the specific context of mobile applications is still unclear. This paper reports on a large empirical study that aims to understand the impact of single and co-occurrent refactorings on quality metrics in Android applications. We analyze the evolution history of 800 open-source Android applications containing a total of 84,841 refactoring operations. We first analyze the impact of single refactoring operations on 21 common quality metrics using the Difference-in-Difference (DiD) statistical model. Then, we identify the most common co-occurrent refactorings using association rule mining, and investigate their impact on quality metrics using the DiD model. Our investigations deliver several important findings. Our results reveal that co-occurrent refactorings are quite prevalent in Android applications. Overall, 60% of the total number of refactoring commits contain multiple refactoring types, and 16 co-occurrent refactoring pairs tend to be applied together leading to a higher impact than single refactorings. We found that single refactorings have no statistically significant impact on quality metrics in 74.7% of the cases, a positive impact in 23.1% of the cases, and a negative impact in 2.2% of the cases. Whereas, co-occurrent refactorings have no statistically significant impact on quality metrics in 54.3% of the cases, a positive impact in 42.4% of the cases, and a negative impact in 3.3% of the cases. Our findings provide practical insights and suggest directions for researchers, practitioners, and tool builders to improve refactoring practices in the context of Android applications development.
LBARNet: Lightweight bilateral asymmetric residual network for real-time semantic segmentation

Real-time semantic segmentation, as a key technique for scene understanding, has been an important research topic in the field of computer vision in recent years. However, existing models are unable to achieve good segmentation accuracy on mobile devices due to their huge computational overhead, which makes it difficult to meet actual industrial requirements. To address the problems faced by current semantic segmentation tasks, this paper proposes a lightweight bilateral asymmetric residual network (LBARNet) for real-time semantic segmentation. First, we propose the bilateral asymmetric residual (BAR) module. This module learns multi-scale feature representations with strong semantic information at different stages of the semantic information extraction branch, thus improving pixel classification performance. Secondly, the spatial information extraction (SIE) module is constructed in the spatial detail extraction branch to capture multi-level local features of the shallow network to compensate for the lost geometric information in the downsampling stage. At the same time, we design the attention mechanism perception (AMP) module in the jump connection part to enhance the contextual representation. Finally, we design the dual branch feature fusion (DBF) module to exploit the correspondence between higher-order features and lower-order features to fuse spatial and semantic information appropriately. The experimental results show that LBARNet, without any pre-training and pre-processing and using only 0.6M parameters, achieves 70.8% mloU and 67.2% mloU on the Cityscapes dataset and Camvid dataset, respectively. LBARNet maintain a high segmentation accuracy while using a smaller number of parameters compared to most existing state-of-the-art models.
Semantic Context and Attention-driven Framework for Predicting Visual Description Utilizing a Deep Neural Network and Natural Language Processing

Background/Purpose: This literature review's goal is to inspect various machine learning algorithms for visual description and their applications to prediction. Examining the numerous approaches mentioned in this area brings up a fresh avenue for expanding the current research methods. Design/Methodology/Approach: The study results that are provided in different scholarly works are acquired from secondary sources, such as scholarly journal publications. This review study analyses these articles and highlights their interpretations. Findings/Result: This research focuses on several cataloguing methods for isolated identifying images and visions. When developing research topics in the idea of inaccessible detecting geographic information systems, the gaps discovered during analysis using various methodologies have made things simpler. Research limitations/implications: This study examined a range of AI tool uses. The scope of this work is rivetted to a assessment of the many machine-learning implementation strategies for analysis and prediction. More research might be done on the many deep learning constructions for image and video classification. Originality/Value: The articles chosen for this study's review are from academic journals and are cited by other authors in their works. The articles that were selected for the examination have a connection to the investigation and research plan described in the paper. Paper Type: Literature review paper.
A numeric-based machine learning design for detecting organized retail fraud in digital marketplaces

Organized retail crime (ORC) is a significant issue for retailers, marketplace platforms, and consumers. Its prevalence and influence have increased fast in lockstep with the expansion of online commerce, digital devices, and communication platforms. Today, it is a costly affair, wreaking havoc on enterprises' overall revenues and continually jeopardizing community security. These negative consequences are set to rocket to unprecedented heights as more people and devices connect to the Internet. Detecting and responding to these terrible acts as early as possible is critical for protecting consumers and businesses while also keeping an eye on rising patterns and fraud. The issue of detecting fraud in general has been studied widely, especially in financial services, but studies focusing on organized retail crimes are extremely rare in literature. To contribute to the knowledge base in this area, we present a scalable machine learning strategy for detecting and isolating ORC listings on a prominent marketplace platform by merchants committing organized retail crimes or fraud. We employ a supervised learning approach to classify postings as fraudulent or real based on past data from buyer and seller behaviors and transactions on the platform. The proposed framework combines bespoke data preprocessing procedures, feature selection methods, and state-of-the-art class asymmetry resolution techniques to search for aligned classification algorithms capable of discriminating between fraudulent and legitimate listings in this context. Our best detection model obtains a recall score of 0.97 on the holdout set and 0.94 on the out-of-sample testing data set. We achieve these results based on a select set of 45 features out of 58.
Overview of intelligent video coding: from model-based to learning-based approaches

Abstract Intelligent video coding (IVC), which dates back to the late 1980s with the concept of encoding videos with knowledge and semantics, includes visual content compact representation models and methods enabling structural, detailed descriptions of visual information at different granularity levels (i.e., block, mesh, region, and object) and in different areas. It aims to support and facilitate a wide range of applications, such as visual media coding, content broadcasting, and ubiquitous multimedia computing. We present a high-level overview of the IVC technology from model-based coding (MBC) to learning-based coding (LBC). MBC mainly adopts a manually designed coding scheme to explicitly decompose videos to be coded into blocks or semantic components. Thanks to emerging deep learning technologies such as neural networks and generative models, LBC has become a rising topic in the coding area. In this paper, we first review the classical MBC approaches, followed by the LBC approaches for image and video data. We also discuss and overview our recent attempts at neural coding approaches, which are inspiring for both academic research and industrial implementation. Some critical yet less studied issues are discussed at the end of this paper.
Riemannian Hamiltonian Methods for Min-Max Optimization on Manifolds

In this paper, we study min-max optimization problems on Riemannian manifolds. We introduce a Riemannian Hamiltonian function, minimization of which serves as a proxy for solving the original min-max problems. Under the Riemannian Polyak–Łojasiewicz condition on the Hamiltonian function, its minimizer corresponds to the desired min-max saddle point. We also provide cases where this condition is satisfied. For geodesic-bilinear optimization in particular, solving the proxy problem leads to the correct search direction towards global optimality, which becomes challenging with the min-max formulation. To minimize the Hamiltonian function, we propose Riemannian Hamiltonian methods (RHMs) and present their convergence analyses. We extend RHMs to include consensus regularization and to the stochastic setting. We illustrate the efficacy of the proposed RHMs in applications such as subspace robust Wasserstein distance, robust training of neural networks, and generative adversarial networks.
Constructing the affectiveness and aesthetics of touch through shape-changing fashion and textiles

This ongoing PhD study aims to explore the potential of shape-changing fashion and textiles for aesthetic haptic interaction and affective soma experiences using Research through Design methodology. The research has progressed through multiple workshops and design projects, with each one building upon the previous findings and contributing to the ongoing work. Design strategies and toolkits are developed to accommodate various interaction modalities, soma experiences, haptic patterns, and use contexts. Preliminary results show the potential of translating affective haptic experiences into fashion and textile design languages. It also contributes to the development of textile-craft-based shape-changing methods for hand-held tactile textile installation and body-worn haptic wearables design to generate engaging experiences. In the future study, I will verify the effectiveness of the developed design toolkits through workshops by engaging design students, thus preparing the study’s outcomes for broader dissemination.
Very-large-scale integration device for parallel vertical group computing the sum of squared differences

Is a paper that proposes a new method for computing sum-of-squares differences in a parallel vertical environment. The method is based on a group approach, which allows you to divide the task into several subtasks and calculate them in parallel. The article considers the problem of calculating the sum of squared differences between elements of large data arrays. Applying traditional methods of calculating such sums in parallel environments can be inefficient due to the exchange of large amounts of data between nodes. The proposed method allows to reduce the amount of transmitted data and increase the efficiency of calculations. The article proposes a new method for calculating the sum of squared differences, which allows to increase the efficiency of calculations in a parallel vertical environment. Testing of the method on different data sets shows its high efficiency compared to traditional methods of calculating sums of squared differences in parallel environments. The proposed method can be applied in various areas that require the processing of large volumes of data, and allows to increase the efficiency of calculations and reduce their execution time. The methods, algorithms and structures of devices for computing the sum of squared differences have been analyzed and their defects have been defined in the article. It has been defined that the device for computing the sum of squared differences should support the next: high device utilization; the use of capabilities and benefits of VLSI; short-term development and moderate price. The development of the device has been suggested by computing the sum of squared differences using modularity principles, coordination between data flow and computing capability of the device, pipelining and space parallelism, localization and simplification of links with elements. The proposed method can be useful for researchers in the fields of parallel computing and data processing, and can find applications in various fields such as data science, machine learning, image processing, and bioinformatics.
DTEC: Decision tree-based evidential clustering for interpretable partition of uncertain data

Recently, the evidential clustering has been developed as a promising clustering framework for uncertain data, which generalizes those hard, fuzzy, possibilistic and rough clustering. However, the resulting cluster assignments are less interpretable in terms of human cognition, which limits its applications in those security, privacy or ethic related fields. In this study, the unsupervised decision tree model is introduced into the evidential clustering framework to improve the interpretability of the evidential partition. A Decision Tree-based Evidential Clustering (DTEC) algorithm is developed to build an unsupervised evidential decision tree, which uses the paths from the root node to leaf nodes to achieve the interpretability of each cluster. The proposed algorithm is composed of three procedures, i.e., cutting-point selection, node evidential splitting, and cluster adjustment, in which the first two procedures are carried out iteratively to build a preliminary unsupervised decision tree and the last procedure is designed to adjust the preliminary decision tree if the number of clusters is available. Both synthetic and real datasets are used to evaluate the performance of the proposed algorithm, and the experimental results demonstrate the good performance of the proposal compared with some representative fuzzy, evidential or decision tree-based clustering algorithms.
Deep feature extraction and compression for human–machine vision

Latent representation features in deep learning (DL) exhibit excellent potential for visual data applications. For example, in traffic monitoring and video surveillance, the features simultaneously perform image analysis for machine vision and image reconstruction for human viewing. However, the existing deep features that appeal to machine and human receivers are always combinations of separated pieces and specific features. Due to these features being extracted from different branches in collaboration frameworks, the inherent relations between machine and human vision are insufficiently explored. Therefore, to obtain one set of representative and generic features, we propose a dynamic groupwise splitting network based on image content to explore and extract generic features for the two different receivers. First, we analyze the characteristics of the latent features and adopt intermediate features as the base features. Then, a feature classification and transformation mechanism based on image content is proposed to enhance the base features for further image reconstruction and analysis. Consequently, an end-to-end model with multimodel cascading and multistage training realizes both machine and human vision tasks. Extensive experiments show that our human–machine vision collaboration framework has high practical value and performance.
A Qualitative Case Study on the Improvement Plan of Education Welfare Priority Support Project: A focus on worker perception

Objectives The purpose of this study is to examine from the perspective of workers how the educational welfare priority support project is being operated, how to improve it, and what direction to move forward. Methods As a research method, a qualitative case study was used that can explore and examine the context of special situations. For a qualitative study of the experience of the educational welfare priority support project, a total of six interviews were conducted with three participants twice each. Results In the current state of educational welfare priority support projects, changes in children and adolescents and the scope of targets were discussed in relation to the target. In practical difficulties, difficulties in collaboration between schools or regions, quantitative and qualitative problems of human resources, and budget-related problems were mentioned. The directions for educational welfare priority support projects should be to establish the philosophical value of education welfare priority support projects, support for customized growth based on resource-linked, improve human resources (competent employee training, mandatory placement of workers), improve policy systems (self-evaluation system, integrated operation), and improve awareness. Conclusions As a result, for the development of educational welfare priority support projects, it is necessary to implement educational welfare priority support projects based on the philosophical value of school social welfare, and to strengthen worker capabilities, improve awareness, establish a monitoring system, expand manpower and budget, and continue research on educational welfare priority support projects.
Pathfinder elements and indicator minerals of Au from the Kubi Gold ore deposits in Ghana

Abstract The Au mineralization in the Kubi Gold Mining Area in the Birimian of Ghana is associated with garnet (about 85 vol.%), magnetite, pyrrhotite, arsenopyrite, and sulfide minerals, as well as quartz with gold and calcite. These minerals and the included elements can act as indicator minerals or pathfinder elements. For the present work, we collected samples from drill holes at different depths, from the alluvial zone (0–45 m) to the ore zone (75–100 m). The distributions of minerals and elements in the rocks that act as indicator minerals and pathfinder elements in the concession area were investigated along the drill hole cross sections. X-ray diffraction shows that the samples contain garnet, pyrite, periclase, and quartz as the main indicator minerals. By energy-dispersive X-ray spectroscopy, Fe, Mg, Al, S, O, Mn, Na, Cu, Si, and K are identified as corresponding pathfinder elements. The results indicate that the Au mineralization in the Kubi Mine area correlates mostly with the occurrence of garnet, pyrite, goethite, and kaolinite in the host rocks, which show towards the surface increasingly hematitic and limonitic alteration in form of Fe(oxy-)hydroxides.
ENHANCING STUDENTS’ LEARNING OUTCOMES: BITS AND PIECES GAME

ABSTRACTThis research analyzes the implementation of the "bits and pieces" game on students' learning outcomes in writing English descriptive texts. A quasi-experimental design with Junior High School students as the population and a cluster random sampling technique used to draw the sample. Furthermore, two groups were administered pre- and post-test to collect the data. According to the data analysis, a noteworthy disparity existed in the post-test outcomes between the two groups. The experimental group had an average score of 86.60, whereas the control group had 62.43 score. By setting a significance level of 0.05 and degree of freedom (df) of 40 (21+21-2), the t-test demonstrated that the calculated t-value (6.487) surpassed the critical value (1.684), thereby indicating the acceptance of the research hypothesis. This implied that bits and pieces games have an effect on enhancing students’ learning outcomes. Therefore, the game can be a great contribution for English teachers in teaching English descriptive text, in terms of improving content, mechanics, organization, sentence structure, and grammar. ABSTRAKPenelitian ini menganalisis implementasi permainan bits dan pieces terhadap hasil belajar siswa dalam menulis teks deskriptif dalam bahasa Inggris. Desain penelitian yang digunakan adalah eksperimen semu. Populasinya siswa SMP dan teknik pengambilan sampel melalui cluster random sampling. Data diambil dari hasil pre- dan post-test pada kelompok kontrol dan eksperimen yang menunjukkan terdapat pengaruh atau peningkatan pada kelompok kontrol dan eksperimen. Nilai rata-rata dari post-test pada kelompok eksperimen sebesar 86.60 dan kelompok kontrol sebesar 62.43 dengan menerapkan tingkat signifikansi 0.05 dan derajat kebebasan (df) 40 (21+21-2), hal ini menunjukkan nilai t-hitung (6.487) lebih besar dari nilai t- tabel (1.684) yang bermakna bahwa hipotesa penelitian ini diterima. Dengan demikian permainan bits dan pieces berpengaruh terhadap peningkatan hasil belajar siswa dalam menulis teks deskriptif. Oleh karena itu, hasil penelitian ini sangat berkontribusi kepada guru Bahasa Inggris yang mengajarkan keterampilan menulis teks deskriptif Bahasa Inggris yang berkaitan dengan peningkatan isi, penggunaan tanda baca, huruf besar dan kecil, pengorganizatian teks deskriptif, dan aturan kata dalam kalimat.How to Cite: Hairul, M.A., Nurhayati. (2023). Exploring Teacher Educators’ Understanding of Critical Pedagogy and its Implementation in the English Reading Class. IJEE (Indonesian Journal of English Education), 10(1), 207-220. doi:10.15408/ijee.v10i1.32312
VARVO: a Novel Method for the Fast Detection of Vehicle Crash Events from Video Only Data

Around 1,35 million people worldwide die each year because of traffic incidents, and it is estimated that another 50 million suffers serious injuries. This picture is particularly dramatic in the Andean Region where the death toll due to traffic accidents is as high as 127 deaths per million inhabitants. In recent years the deployment of the so-called Intelligent Transport Systems (ITS) across several developed countries has helped to reduce the number of deaths due to traffic accidents. An integral part of an ITS is the automatic detection of traffic incidents from video and sensor data. However, the scarcity of curated datasets, especially those that contained a reasonable number of positive instances of traffic incidents is hampering the development of artificial intelligence applications for the domain of traffic research. Given this scenario, we pursued answering the following research question: is it possible to detect car crashes through supervised machine learning based on the estimated speeds of cars from video only-data? Here we present VARVO, a novel algorithm for the detection of traffic incidents that does not rely on sensors for cars speed detection. VARVO performs a supervised classification task based on the sequential use of convolutional network-based object detection and bi-directional tracking. We also describe how the models implemented in VARVO improved their classification accuracy by applying an oversampling algorithm to deal with class imbalance. We believe that the deployment of VARVO could be linked to static traffic video cameras and could be part of the Intelligent Transport Systems foundations in other Andean countries.