Reviewerly/exHarmony huggingface dataset:
Resilient Strategies for Internet-Based Education: Investigating Engineering Students in the Canary Islands in the Aftermath of COVID-19

The COVID-19 pandemic has brought about notable changes in the education sector, specifically the shift towards online learning. This study examined the experiences of 124 engineering students in the Canary Islands, an EU ultra-peripheral region, as they adapted to online education during the pandemic. A comprehensive survey assessed students' experiences in five key dimensions, including satisfaction with traditional face-to-face learning, perceptions of the engineering department's transition to online learning, module-specific adaptations, personal adaptation strategies, and the adaptation of teaching staff. The study's methodology involved statistical analyses using Microsoft Excel v16.0 and SPSS 27 tools to identify patterns and draw conclusions. The findings indicate a nuanced landscape. Students demonstrated strong technological literacy and readiness for online learning. However, they expressed concerns about educators' digital proficiency and perceived a decline in educational quality. These results emphasize the critical need for sustainable, adaptable, and inclusive educational strategies, particularly in regions like the Canary Islands that face unique challenges. The implications of the study have broader relevance to digital education. It is essential to note the need for educators to receive comprehensive training in digital tools and methodologies to improve the quality of online learning.
Social Media and Higher Education: A Linguistic Analysis of Identity Construction of Pakistani Universities

This study rigorously explores the linguistic strategies employed by Pakistani universities for identity construction on social media platforms, focusing primarily on X (formerly known as Twitter). Utilizing a robust methodology of corpus-assisted discourse analysis, we scrutinized a substantial dataset comprising 2861 tweets. These were collected from the official X accounts of two leading Pakistani universities, National University of Sciences and Technology (NUST) and Lahore University of Management Sciences (LUMS), during the academic year 2021/2022. Our in-depth analysis uncovered distinctive linguistic patterns, particularly in the use of pronouns and specific multi-word key terms that each university employs to construct its identity. Intriguingly, the linguistic choices made by these institutions were found to significantly shape their digital identities, reflecting both individuality and broader cultural themes. The study augments existing research in the field by offering novel and context-specific insights into the dynamics of identity construction via social media. It underscores the pivotal role language plays in the organizational branding landscape. Furthermore, the findings from this research serve as a valuable resource for informing and refining communication strategies, not only for educational institutions but also for organizations at large that aim to construct and effectively manage their digital identities on social media platforms.
Special Issue on Transportation Big Data and Its Applications

Large-scale traffic sensors are strategically deployed across various infrastructures and modes of transportation (e [...]
Research on Imbalanced Data Regression Based on Confrontation

The regression model has higher requirements for the quality and balance of data to ensure the accuracy of predictions. However, there is a common problem of imbalanced distribution in real datasets, which directly affects the prediction accuracy of regression models. In order to solve the problem of data imbalance regression, considering the continuity of the target value and the correlation of the data and using the idea of optimization and confrontation, we propose an IRGAN (imbalanced regression generative adversarial network) algorithm. Considering the context information of the target data and the disappearance of the deep network gradient, we constructed a generation module and designed a composite loss function. In the early stages of training, the gap between the generated samples and the real samples is large, which easily causes the problem of non-convergence. A correction module is designed to train the internal relationship between the state and action as well as the subsequent state and reward of the real samples, guide the generation module to generate samples, and alleviate the non-convergence of the training process. The corrected samples and real samples are input into the discriminant module. On this basis, the confrontation idea is used to generate high-quality samples to balance the original samples. The proposed method is tested in the fields of aerospace, biology, physics, and chemistry. The similarity between the generated samples and the real samples is comprehensively measured from multiple perspectives to evaluate the quality of the generated samples, which proves the superiority of the generated module. Regression prediction is performed on the balanced samples processed by the IRGAN algorithm, and it is proven that the proposed algorithm can improve the prediction accuracy in terms of the imbalanced data regression problem.
Interval Type-2 Fuzzy-Model-Based Sampled-Data Control of an AUV Depth System with Input Saturation

This paper proposes a sampled-data fuzzy controller design technique for an autonomous underwater vehicle (AUV) depth system represented by an interval type-2 (IT-2) fuzzy model, considering input saturation. In the Takagi–Sugeno (T–S) fuzzy model of an AUV depth system, surge velocity is chosen as a premise variable. To address the associated uncertainty with this variable, we employ the IT-2 fuzzy modeling technique. Also, the controller proposed in this paper utilizes time-varying gains, ensuring superior exponential stability compared with traditional fixed gain approaches. Furthermore, a membership function-dependent (MFD) H∞ criterion is employed to enhance robustness for each subsystem individually. Taking into account the mentioned aspects, the controller design condition is derived in the form of linear matrix inequalities (LMIs). Finally, the effectiveness of the proposed method is validated through simulation examples.
Demonstration Training and Test Datasets for a 60/40 split that passes univariate KS 2 sample test and WALD test.

&lt;p dir="ltr"&gt;Random random seeds are used, but captured. The KS 2 sample univariate tests are then performed. All columns must pass.&lt;/p&gt;&lt;p dir="ltr"&gt;If the tests are passed, the WALD test is performed and the WALD test statistic scores captured. The dataset with the best WALD p-value is then selected, with the accompanying random seed for reproducibility.&lt;/p&gt;
Developing Public Speaking Ability For the Teachers of SMP Jamiah Al-Aziziyah Batee Iliek Samalanga

Teachers are the backbone of an educational institution. The progress or setback of an educational institution is determined by the role of teachers. Through observations and interviews conducted with the Headmaster of SMP Jamiah Al-Aziziyah Batee Iliek, it was found that the majority of teachers lack proficient public speaking skills. Based on this issue, it is necessary to organize activities to enhance public speaking skills, which are crucial for the teachers of SMP Jamiah Al-Aziziyah Batee Iliek. The method of implementing these activities is through lecture-based methods. The stages of the activities include pre-implementation, implementation, and evaluation. Technically, a theoretical explanation related to public speaking skills is provided, and participants then engage in practical exercises. After the lectures, participants are assigned tasks and provided guidance to create speech scripts on themes relevant to the school's needs. The results of the community service activities aimed at enhancing public speaking skills have shown improvement among the participants. According to the participants' questionnaire responses, there was an increase in understanding the criteria for effective speakers, with 20% previously not understanding and 80% having an average level, which improved to 40% understanding and 60% having a high level of understanding. During the practical exercises, all participants successfully overcame confidence issues while speaking. The results of the task to compose speech scripts were quite satisfactory, with an average score of 77.5 out of 100.
Leveraging Chain-of-Thought to Enhance Stance Detection with Prompt-Tuning

Investigating public attitudes towards social media is crucial for opinion mining systems to gain valuable insights. Stance detection, which aims to discern the attitude expressed in an opinionated text towards a specific target, is a fundamental task in opinion mining. Conventional approaches mainly focus on sentence-level classification techniques. Recent research has shown that the integration of background knowledge can significantly improve stance detection performance. Despite the significant improvement achieved by knowledge-enhanced methods, applying these techniques in real-world scenarios remains challenging for several reasons. Firstly, existing methods often require the use of complex attention mechanisms to filter out noise and extract relevant background knowledge, which involves significant annotation efforts. Secondly, knowledge fusion mechanisms typically rely on fine-tuning, which can introduce a gap between the pre-training phase of pre-trained language models (PLMs) and the downstream stance detection tasks, leading to the poor prediction accuracy of the PLMs. To address these limitations, we propose a novel prompt-based stance detection method that leverages the knowledge acquired using the chain-of-thought method, which we refer to as PSDCOT. The proposed approach consists of two stages. The first stage is knowledge extraction, where instruction questions are constructed to elicit background knowledge from a VLPLM. The second stage is the multi-prompt learning network (M-PLN) for knowledge fusion, which learns model performance based on the background knowledge and the prompt learning framework. We evaluated the performance of PSDCOT on publicly available benchmark datasets to assess its effectiveness in improving stance detection performance. The results demonstrate that the proposed method achieves state-of-the-art results in in-domain, cross-target, and zero-shot learning settings.
First-Order Logic with Adverbs

This paper introduces two languages and associated logics designed to afford perspicuous representations of a range of natural language arguments involving adverbs and the like: first-order logic with basic adverbs (FOL-BA) and first-order logic with scoped adverbs (FOL-SA). The guiding logical idea is that an adverb can come between a term and the rest of the statement it is a part of, resulting in a logically stronger statement. I explain various interesting challenges that arise in the attempt to implement the guiding idea, and provide solutions for some but not all of them. I conclude by outlining some directions for further research.
Spatial Relation Awareness Module for Phase Unwrapping

Phase unwrapping is a technique used to recover the original phase from the wrapped phase in the range (−π,π]. Various methods have been proposed for phase unwrapping. In particular, methods using convolutional neural networks (CNNs) have been extensively researched because of their high robustness against noise and fast inference speed. However, conventional CNN-based methods discard the local position information and relationships between pixels in the convolution process, resulting in poor phase-unwrapping performance. To obtain better phase unwrapping results, we propose a module that combines a global convolution network, which applies convolutional layers with a kernel size equivalent to that of the feature maps, and CoordConv, which acquires the positional relationships between pixels. We validated the performance of the proposed method by comparing it with a quality-guided path algorithm and deep learning-based phase unwrapping methods and found that the proposed method is highly robust against noise.
Exploring JASP as a data analysis tool in L2 research: a snapshot

This paper explores the potential of JASP (Jeffreys’s Amazing Statistics Program, https://jasp-stats.org/) as a robust statistical tool in advancing Second Language Acquisition (SLA) research, with a specific emphasis on its application within the domain of L2 writing. Second language writing proficiency is a complex and multifaceted skill, demanding rigorous empirical investigation to uncover nuanced patterns and insights. JASP, known for its user-friendly interface and advanced statistical capabilities, emerges as a promising instrument for researchers seeking to unravel the intricacies of L2 writing development. The paper begins by providing an overview of the features embedded in JASP and continues to discuss some of the extant research within the field of SLA that implements JASP as a data analysis tool. Follows is a detailed description of the use of JASP in two L2 writing papers, in which the data analysis decisions are discussed. Furthermore, the discussion delves into the practical implications of utilizing JASP in L2 writing research, including its ability to accommodate small sample sizes, handle complex interactions, and facilitate transparent and reproducible analyses. The paper concludes by advocating for the widespread adoption of JASP in SLA research, positing that its integration holds the promise of advancing our understanding of the intricacies of L2 writing development and contributing to the refinement of pedagogical approaches in second language education.
A Hierarchical Orthographic Similarity Measure for Interconnected Texts Represented by Graphs

Similarity measures play a pivotal role in automatic techniques designed to analyse large volumes of textual data. Conventional approaches, treating texts as paradigmatic examples of unstructured data, tend to overlook their structural nuances, leading to a loss of valuable information. In this paper, we propose a novel orthographic similarity measure tailored for the semi-structured analysis of texts. We explore a graph-based representation for texts, where the graph’s structure is shaped by a hierarchical decomposition of textual discourse units. Employing the concept of edit distances, our orthographic similarity measure is computed hierarchically across all components in this textual graph, integrating precomputed similarity values among lower-level nodes. The relevance and applicability of the presented approach are illustrated by a real-world example, featuring texts that exhibit intricate interconnections among their components. The resulting similarity scores, between all different structural levels of the graph, allow for a deeper understanding of the (structural) interconnections among texts and enhances the explainability of similarity measures as well as the tools using them.
Design of a Generic Dynamically Reconfigurable Convolutional Neural Network Accelerator with Optimal Balance

In many scenarios, edge devices perform computations for applications such as target detection and tracking, multimodal sensor fusion, low-light image enhancement, and image segmentation. There is an increasing trend of deploying and running multiple different network models on one hardware platform, but there is a lack of generic acceleration architectures that support standard convolution (CONV), depthwise separable CONV, and deconvolution (DeCONV) layers in such complex scenarios. In response, this paper proposes a more versatile dynamically reconfigurable CNN accelerator with a highly unified computing scheme. The proposed design, which is compatible with standard CNNs, lightweight CNNs, and CNNs with DeCONV layers, further improves the resource utilization and reduces the gap of efficiency when deploying different models. Thus, the hardware balance during the alternating execution of multiple models is enhanced. Compared to a state-of-the-art CNN accelerator, Xilinx DPU B4096, our optimized architecture achieves resource utilization improvements of 1.08× for VGG16 and 1.77× for MobileNetV1 in inference tasks on the Xilinx ZCU102 platform. The resource utilization and efficiency degradation between these two models are reduced to 59.6% and 63.7%, respectively. Furthermore, the proposed architecture can properly run DeCONV layers and demonstrates good performance.
Sunda Script Detection Using You Only Look Once Algorithm

The Sundanese script is a writing system used in the Sundanese language, one of the regional languages of West Java, Indonesia. This study investigates the use of the YOLO v8 algorithm for the real-time video detection of Sundanese script. Various versions of YOLO v8, including YOLO v8n, v8s, v8m, v8l, and v8x, were tested to determine the most effective model. After a comprehensive evaluation involving the analysis of mean Average Precision (mAP), F1-Confidence, and precision, the study selected the YOLO v8s model as the primary detection method. YOLO v8s demonstrated superior performance with the highest mAP of 98.835%, an F1-Confidence of 98%, and a precision of 76,2%. This choice was based on a balance between high accuracy and computational efficiency. The results indicate significant potential for object recognition technology in the learning and preservation of Sundanese script.
Web-Based Tuition Payments Information System At Al-Wafi Kindergarten

The tuition payment system at AL-WAFI Kindergarten is still done manually, that is, recording is done using a tuition payment notebook. Which can cause errors in recording SPP payment data or mistakes in calculating SPP payments, the process of recording and making SPP payment reports takes a long time. Several other problems were also found that were often complained about, namely the difficulty of collecting separate SPP payment data in several financial books. This slows down the payment process, recording and reporting payments. This causes the process related to SPP payments to not run optimally. To overcome all the problems above, the author proposes to design and build supporting applications such as database systems so that processing of tuition payments is more effective and efficient, and can also simplify operational activities in schools. This system was designed and built using the PHP (Hypertext Preprocessor) programming language and MYSQL as the database.
Pattern Recognition in Medical Images Through Innovative Edge Detection with Robert's Method

This research introduces an innovative approach for pattern recognition in medical images through the application of Robert's edge detection method. Pattern recognition in medical images has great significance in disease diagnosis and patient care management. Edge detection is an important stage in image processing which aims to determine the boundaries of objects in the image. Robert's edge detection method is one of the classic methods that has been used in image processing. However, improving edge detection performance is needed to improve accuracy in pattern recognition in medical images. In this study, we propose a modified variation of Robert's method to increase the accuracy in finding edges in medical images. The proposed innovative approach is tested using a large and diverse medical image dataset. Evaluation is carried out by comparing the edge detection results using the conventional Robert method with the results using the proposed modified method. Quantitative analysis is carried out to measure the performance improvements achieved. Experimental results show that the modified Robert edge detection method produces significant improvements in precision and accuracy in finding edges in medical images. These results indicate that the proposed innovative approach has the potential to improve pattern recognition in medical images and can make valuable contributions in the diagnosis and management of diseases.
Implementation of the Laplacian of Gaussian Algorithm in Edge Detection Image Processing of Zebra Cross Damage on Highways in the Langkat Regency Area

Walking is part of the traveler's movement and is the simplest means of transportation, but it is in a weak position and prone to conflict or accidents when they mix with other modes of transportation. To protect pedestrians, special facilities are needed, one of which is a crossing place (zebra crossing) that is able to serve according to pedestrian needs. Based on Law No. 22 of 2009 concerning Traffic Polytechnic Land Transportation Bali 46 Cross and Road Transportation, article 131 paragraph (2), it is stated that "Pedestrians are entitled to priority when crossing the road at the crosswalk". One of the important meanings for human life is the Way. Roads are used as a means of transportation that has a very useful role in efforts to develop human life. In 2018, based on statistical data, the number of motorized vehicle users in Indonesia is increasing every year to reach 146,858,759 units. The impact that occurs is that there are many Zebra Cross roads damaged with conditions that are very troubling and worrying for road users. Among the causes of zebra crossing being damaged will be traffic accidents where the vehicle does not lag obeying the path of the vehicle following the predetermined lane. So this study detects image processing with the Laplacian of Gaussian algorithm with edge detection making it easier for the government to improve traffic signs of zebra crossing images on highways that are worthy of improvement so that accidents do not occur. The results of this study illustrate the image of being able to see damaged zebra crossings with calculations of the Laplacian of Gaussian algorithm.
An Informed-Bi-Quick RRT* Algorithm Based on Offline Sampling: Motion Planning Considering Multiple Constraints for a Dual-Arm Cooperative System

Aiming to address problems such as low sampling success rate and long computation time in the motion planning of a dual-arm cooperative system with multiple constraints, this paper proposes an Informed-Bi-Quick RRT* algorithm based on offline sampling. First, in the process of pre-sampling, the new algorithm relaxes the approximation of constrained manifolds by introducing the idea of incremental construction, and it incorporates the stochastic gradient descent method to replace global random sampling with local random sampling, which enriches the data set and shortens the offline sampling time of the data set. Second, the new algorithm improves the original Quick-RRT* algorithm by combining the two-tree idea and the multi-target bias expansion strategy, and it improves the adaptability of the algorithm to different obstacle environments. In addition, the loosely constrained motion and tightly constrained motion in the two-arm cooperative system are analyzed, and the adaptive planning of the two-arm trajectory in different motions is described in detail. In this paper, the two-arm cooperative model constructed with UR5 and UR10 robot arms is studied, and the ability of the proposed algorithm to deal with multiple constraints is verified by simulating assembly and handling tasks. The experimental results show that compared with other methods, the proposed algorithm has obvious advantages in path quality and planning efficiency.