Reviewerly/exHarmony huggingface dataset:
Improving Software Effort Estimation models using Grey Wolf Optimization Algorithm

One of the phases in the Software Development Life Cycle is planning the software project. Estimating the software effort is another task in this project planning phase. Software effort estimation is the method of determining how many workers are required to create a software project. To increase the precision of software effort estimation, many researchers have focused on this field and used both algorithmic and non-algorithmic techniques. The most widely used method is the Constructive Cost Model (COCOMO). However, the COCOMO model has a limitation, which is related to the precision of the software effort estimation. Meta-heuristic algorithms are preferred with parameter optimization because they can provide solutions that are nearly optimal at a reasonable cost. This study aims to enhance the precision of effort estimation by modifying the three COCOMO-based models’ coefficients and assess the efficiency of Grey Wolf Optimization (GWO) in finding the optimal value of effort estimation through applying four other algorithms, including Zebra Optimization(ZOA), Moth-Flame Optimization(MFO), Prairie Dog Optimization(PDO), and White Shark Optimization(WSO) with NASA18 dataset. These models include the basic COCOMO model, and another two models were also suggested in the published research as a modification of the basic COCOMO model. To assess the performance of the proposed models, the six most used software effort estimation metrics are used. The results demonstrated that the GWO outperformed other algorithms based on high accuracy and significant error minimization, involving ZOA, MFO, PDO, WSO, and other existing models.
Blind Quality Enhancement for Compressed Video

Deep convolutional neural networks (CNNs) have achieved impressive success in enhancing the quality of compressed images/videos. These approaches mostly obtain the noise level in advance and train multiple architecture-identical models for enhancement on images/videos of known levels of noise. It largely hinders their practical applications where the noise level is unknown and resource is limited. To practically perform quality enhancement, we propose a novel blind quality enhancement framework for compressed video (BQEV), which utilizes a single network to conduct enhancement on videos compressed at various and unknown quality parameters (QPs). Since there exists feature similarity and difference among videos compressed at multiple QPs, BQEV utilizes this prior to efficiently handle enhancement on videos compressed at blind QPs, which consists of progressive feature extraction and QP-adaptive feature fusion subnets. They utilize temporal information and feature similarity to progressively extract valuable features and further employ the feature difference to conduct reasonable QP-adaptive feature fusion and quality enhancement, respectively. In the progressive feature extraction subnet, we first design a quality rank module to assign more attention to higher-quality frames for efficient utilization of temporal information, then propose a progressive extraction module to further extract features from different QPs. In the QPadaptive feature fusion subnet, we develop a quality estimation module to guide reasonable feature fusion of these extracted progressive features for stable and promising enhancement results on multiple QPs. Experimental results demonstrate that BQEV achieves 0.31 – 0.69 dB PSNR improvement compared with videos compressed at various QPs, outperforming state-ofthe-art approaches.
Motion planning method for car-like autonomous mobile robots in dynamic obstacle environments

Motion planning between dynamic obstacles is an essential capability to achieve real-world navigation. In this study, we investigated the problem of avoiding dynamic obstacles in complex environments for a car-like mobile robot with an incompletely constrained <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Ackerman</i> front wheel steering. To address the problems of weak dynamic obstacle avoidance and poor path smoothing in motion planning with the traditional Timed Elastic Band (TEB) algorithm, We proposed a hybrid motion planning algorithm (TEB-CA,Timed Elastic Band-Collision Avoidance) that combines an improved traditional TEB algorithm and Optimal Reciprocal Collision Avoidance (ORCA) model to improve the ability of the robot to predict dynamic obstacles in advance and avoid collisions safely. Moreover, We also add new constraints to the traditional TEB algorithm, including: jerk constraints, smoothness constraints, and curvature constraints. The algorithm is implemented in C++ and evaluated experimentally in the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Gazebo</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Rviz</i> simulation environments of the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Robot Operating System (ROS)</i> , as well as in actual experimental tests on our car-like autonomous mobile robot " <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Little Ant</i> " which proves the effectiveness of the method, and that the motion planning scheme is more effective in avoiding dynamic obstacles than the traditional TEB and DWA algorithms.
LSTM-Autoencoder Based Incremental Learning for Industrial Internet of Things

Edge-based intelligent data analytics supports the Industrial Internet of Things (IIoT) to enable efficient manufacturing. Incremental learning in the edge-based data analytics has the potential to analyze continuously collected real-time data. However, additional efforts are needed to address performance, latency, resource utilization and storage of historical data challenges. This paper introduces an incremental learning approach based on Long-Short Term Memory (LSTM) autoencoders, by sparsening the weight matrix and taking samples from previously trained sub-datasets. The aim is to minimize the resources utilized while training redundant knowledge for edge devices of IIoT. The degree of sparsity can be determined by the redundancy of patterns, and the inverse of the coefficient of variation has been utilized to recognize it. A higher value of the inverse of the coefficient of variation shows that the values of the weight matrix are close to each other, which indicates the redundancy of knowledge, and vice versa. In addition, the coefficient of variation has been applied for limiting the size of samples from the previously trained sub-datasets. The experiment conducted using the IIoT testbed dataset demonstrates substantial enhancements in resource optimization without compromising performance.
The Effect of Chatbots and AI on The Self-Efficacy, Self-Esteem, Problem-Solving and Critical Thinking of Students

This article delves into the multifaceted impacts of chatbots and AI in educational settings. It explores how these technologies, increasingly integrated into learning environments, influence key psychological aspects and cognitive skills among students. The review highlights the potential of chatbots in enhancing academic processes, offering personalized learning experiences, and serving as bridges to educational resources. However, it also raises concerns about the ethical use of such technologies. Focusing on psychological aspects, the article reviews literature suggesting that frequent and satisfying interactions with chatbots can enhance students' self-efficacy and engagement. Studies indicate that chatbots might improve self-efficacy in experimental settings and have indirect effects on health-related self-efficacy. In terms of self-esteem and self-confidence, the research presents mixed findings. While chatbots can positively affect body image and self-esteem among certain demographics, over-reliance on these technologies for social interaction or validation might negatively impact real human connections and individual confidence. The article also examines the impact of chatbots on problem-solving skills. Some studies suggest that AI chatbots can enhance problem-solving abilities, especially when integrated into educational systems. However, there is a risk that reliance on chatbots could limit users' exploration of alternative problem-solving strategies. Critical thinking is another area reviewed, with studies presenting diverse results. While some research indicates a positive influence of chatbots on critical thinking, others suggest limitations or context-dependent effects. The article concludes that while AI and chatbots offer transformative potential for enhancing student learning and engagement, their impact is complex and multifaceted. Future advancements in chatbot technology should aim to enhance their positive impact on users' psychological well-being and cognitive development, balancing the need for independent thinking and adaptability to complex problems.
Material modules based on traditional games “Egrang” to improve students' conceptual understanding

This research aimed was to develop a module on work and energy based on the traditional game of "egrang" that could improve conceptual understanding. The development of the module was carried out using the Research and Development (R&amp;D) method by adapting the 4D development model. The defining stage was conducted by analyzing the syllabus, analyzing the learning objectives, and setting the indicators for basic competency achievement. The design stage was carried out by preparing the module. The development stage involved testing the feasibility of the module's content and language. The dissemination stage was carried out by publishing a scientific article in a journal related to the development of learning tools. The work and energy module based on the traditional game of "egrang" that was produced was validated by two expert validators. The validation results showed that the work and energy module based on the traditional game of "egrang" had an average percentage score of 82.95% for content and 87.5% for language, thus making it highly suitable for use. The work and energy module based on the traditional game of "egrang" can be further improved based on the feedback received and implemented in classroom learning.
THE AHP APPROACH FOR DETERMINING E-LEARNING PLATFORM IN UNIVERSITY

Physical distancing policy as the response to pandemic covid-19 has forced the educational institutes to provide online learning for the students. The students and lecturer have to adapt to using online platforms to continue the learning activities. An E-learning platform is needed for distance learning. This research aims to analyze the preference of e-learning platforms among Google Classroom, Youtube, Whatsapp, and Zoom for students in a private university in Yogyakarta, Indonesia, using the Analytical Hierarchy Process approach. Several criteria are formulated for determining the e-learning platform based on the students’ common difficulties during online-based learning, namely service of platform, learning comprehension, internet usage, and network strength. The result shows that learning comprehension has the highest weight (0.363), followed by network strength (0.258), internet usage (0.230), and platform service (0.149). Learning comprehension has become the most important criterion that students consider on selecting an e-learning platform. The final result from the Analytical Hierarchy Process approach proposes the best alternative e-learning platform for students based on the previous criteria. Based on the research, Whatsapp is an e-learning platform with the highest weight of 0.359. Therefore, it is determined for recommendation as to the most suitable e-learning platform for students. It has a high local weight based on each criterion considered in selecting an e-learning platform. The result of this study is expected to be input for lecturers and education institutions to evaluate the platforms they use and e-learning processes.
A contrastive analysis of Korean-Indonesian phonological structures

This paper discusses the contrastive analysis of Korean-Indonesian phonological structure. The contrastive analysis here covers phonological correspondence and phonotactic structure between Korean and Indonesian. It also provides implications to pronunciation learning, especially Korean language learners in Indonesia. This paper is a descriptive qualitative research, using literature study from books, journals, and other related resources. This study revealed that Korean had more monophthongs and diphthongs than Indonesian, with Correspondence between Indonesian’s ‘ə’ and Korean’s ‘ɨ’, Korean’s ‘ə (ㅓ), o (ㅗ)’ and Indonesian’s ‘o’, and Korean’s ‘e (ㅔ), ɜ (ㅐ)’ and Indonesian’s ‘e’. However, Korean and Indonesian had different classification of consonant’s manner of articulation, especially on correspondence between voicing and affricative markedness. On syllable structure, Korean was considered to have stricter syllable structure than Indonesian, as seen on constraint applied on syllable. These findings will help instructor create more effective pronunciation learning, with a comprehensive syllabus in using phonological theory and language skills. However, the suprasegmental aspect and phonological process are excluded from this research.
MOTIVATION SYSTEM FEATURES FORMATION OF PEDAGOGICAL STAFF DEVELOPMENT

The article highlights the urgency of the problem of forming a system of motivation for the development of teaching staff. The authors emphasize the need to create a quality system of staff motivation in educational institutions. Emphasis is placed on the importance in modern conditions of creating an appropriate motivational basis that will be able to motivate teachers to effective professional activity and the provision of quality educational services. The article partially describes the use of innovative forms and methods of stimulating teachers, which will contribute to high productivity. The authors analyze the main tasks of the system of motivation of professional activity of teachers at the level of strategy of development of general secondary education institutions. The priority conditions for the development of the system of motivation of teachers are determined, the important indicator of the ratio and importance of the influence of motivating and supporting factors of the external and internal environment is characterized.&#x0D; Researchers claim that the motivational sphere in the management of educational institutions is a necessary element of the overall management system, is an extremely mobile and dynamic structure. The authors substantiate the importance of creating a favourable environment for motivating the development of teachers, which will provide an atmosphere of inspiration.
Adapting to the Dark: A Novel Adaptive Low Light Illumination Correction Algorithm for Video Sequences in Wireless Communications

The lower image quality and higher noise levels of low light images can have a substantial effect on wireless communication. Images typically have less brightness, less clarity, and more picture noise in low light situations. The precision and dependability of picture-based communication may be impacted by this decline in image quality. Low light levels can be detrimental to video chats since it becomes difficult for the camera to get clear pictures of the user's face. This may lead to decreased video quality, trouble with facial recognition, and a generally worse user experience when communicating over video. Low light illumination correction algorithms are frequently made to work quickly and effectively in order to enable real-time video-based wireless communication. Correcting uneven illumination or low light in images is crucial for various fields; as such images pose challenges for human recognition, computer vision algorithms, and multimedia algorithms. An adaptive illumination correction algorithm based on a simple log transformation approach enhanced by a hyperbolic beta transformation and a logarithmic image processing is presented in this research. It has an adaptive optimization function that adjusts itself based on the image’s illumination and reflectance. Low computation and minimal parameter optimization are the hallmarks of this method. A variety of illuminated images and video sequences from various datasets are used to evaluate the suggested adaptive illumination approach. This adaptive illumination approach's experimental findings are impressive compared to those produced using prior techniques. The results reveal that the proposed system can adapt to various image-based communication and surveillance video sequences taken under various illumination conditions.
Research on the Innovation of English Teaching Mode Based on Artificial Intelligence

This article presents a research on innovative approaches in English language teaching through the application of Artificial Intelligence (AI). The aim is to address the issues found in traditional English language teaching methods by utilizing AI technology. The article begins by identifying the problems in traditional English language teaching and exploring the current applications of AI in education. Then, it specifically discusses the application of AI technology in English language teaching, such as interactive listening training, intelligent error correction and personalized post-class guidance, and cloud-based intelligent English translation teaching. Subsequently, a proposal is put forward to construct a new English language teaching system based on AI technology, including modules for engaging explanations, interactive English assignments, and creating scenarios for cross-cultural communication oral training. Lastly, an evaluation method for assessing the innovation of English language teaching models based on AI is introduced, which includes feedback and perceptions from both teachers and students, as well as an assessment of teaching outcomes. The research findings of this article can provide a more intelligent, personalized, and effective English language teaching approach, thus positively impacting the quality of English language teaching and enhancing students’ interest in learning.
VEHICLE OCCLUSION REMOVAL FROM SINGLE AERIAL IMAGES USING GENERATIVE ADVERSARIAL NETWORKS

Abstract. Removing occluding objects such as vehicles from drivable areas allows precise extraction of road boundaries and related semantic objects such as lane-markings, which is crucial for several applications such as generating high-definition maps for autonomous driving. Conventionally, multiple images of the same area taken at different times or from various perspectives are used to remove occlusions and to reconstruct the occluded areas. Nevertheless, these approaches require large amounts of data, which are not always available. Furthermore, they do not work for static occlusions caused by, among others, parked vehicles. In this paper, we address occlusion removal based on single aerial images using generative adversarial networks (GANs), which are able to deal with the mentioned challenges. To this end, we adapt several state-of-the-art GAN-based image inpainting algorithms to reconstruct the missing information. Results indicate that the StructureFlow algorithm outperforms the competitors and the restorations obtained are robust, with high visual fidelity in real-world applications. Furthermore, due to the lack of annotated aerial vehicle removal datasets, we generate a new dataset for training and validating the algorithms, the Aerial Vehicle Occlusion Removal (AVOR) dataset. To the best of our knowledge, our work is the first to address vehicle removal using deep learning algorithms to enhance maps.
A Summary of Unsupervised Learning Methods

This chapter summarizes the relationships and characteristics of eight commonly used machine learning methods in detail, namely clustering methods (including hierarchical clustering and k-means clustering), Singular Value Decomposition (SVD), Principal Component Analysis (PCA), Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA), Markov Chain Monte Carlo method (MCMC, including Metropolis–Hastings algorithm and Gibbs sampling), Latent Dirichlet Allocation (LDA), and PageRank algorithm.
The PageRank Algorithm

This chapter first defines the PageRank, and then describes the computation method of PageRank, including the commonly used power method.
Latent Dirichlet Allocation

This chapter first introduces the Dirichlet distribution, then describes the latent Dirichlet distribution model, and finally presents the algorithms of the Latent Dirichlet allocation (LDA) model, including Gibbs sampling and the variational EM algorithm.
Latent Semantic Analysis

This chapter introduces an unsupervised learning method—Latent Semantic Analysis (LSA), first describing the word vector space model and the topic vector space model, followed by the SVD algorithm for LSA, and the Non-negative matrix factorization (NMF) algorithm.
Summary of Supervised Learning Methods

This chapter summarizes the characteristics of ten supervised learning methods, including the perceptron, k-Nearest-Neighbor (k-NN), the Naïve Bayes method, the decision tree, logistic regression and maximum entropy model, Support Vector Machine (SVM), Boosting, the EM algorithm, Hidden Markov Model (HMM), and Conditional random field (CRF).
Boosting

This chapter first introduces the idea of the boosting method and the representative boosting algorithm AdaBoost, then discusses why AdaBoost can improve learning accuracy through training error analysis, then interprets AdaBoost from the perspective of the forward stepwise addition model, and finally describes more specific examples of the lifting method—Boosting tree.
Probabilistic Latent Semantic Analysis

This chapter introduces the models of Probabilistic Latent Semantic Analysis (PLSA), including generative and co-occurrence models, and then describes the learning strategies and algorithms of PLSA models.
Hidden Markov Model

This chapter first introduces the basic concepts of the Hidden Markov Model, and then describes the probability calculation algorithms, learning algorithms, and prediction algorithms of the HMM, respectively.
Investigating the Relationship between Students’ L2 Grammar Competence and Social Media Use

Social media has been gaining popularity in the educational sector recently. Whether scholars support its educational use because of its benefits or whether they reject its use because of the stigma surrounding it, the use of social media has become quite a trend in the educational field. This study investigated the use of social media in relation to the grammatical competence of third year Moulay Ismail University, school of arts and humanities’ English department students. The two variables in question in this study were investigated quantitatively through the use of a survey that consisted of; first a grammatical test to test the students’ grammatical competence and second some statements that students had to answer; the statements were based on a 5 scale Likert scale. The findings of this study showed that the use of social media does influence the grammatical competence of Moroccan learners. Moreover, the different statistical tests proved that the two variables have a significant relationship.