Reviewerly/exHarmony huggingface dataset:
Hand Gesture Vocalizer using MobileNetV2 for Deaf Mutes

To demonstrate communication or interaction with the Deaf and Mute public is difficult nowadays. Gestures are the physical movement form enacted by an individual to transmit some significant information. There are diverse signs which express convoluted meanings and recognizing them is a challenging task for normal people who have no understanding of the sign language. With the quick evolution of technologies, some techniques can be used to identify hand gestures and transform them into voice and text. This document presents the strategy that would assist our assignment in recognizing the various signs. The pictures of both hands are loaded during runtime. This process has been acquired by concerning one user. The images will be apprehended initially and accumulated in the manually and lately caught images and characteristic extraction will be done to determine which indications has been communicated. It consists of two phases: Hand Gesture Recognition and Conversion.
A Fast Adaptive Binarization Method for QR Code Images Based on Dynamic Illumination Equalization

The advancement of Internet of Things (IoT) has enhanced the extensive usage of QR code images in various computer vision applications. Nonetheless, this has also brought forth several technical challenges. In particular, the logistics sorting system often encounters issues such as a low recognition rate and slow processing speed when dealing with QR code images under complex lighting conditions like uneven illumination. To address these difficulties, a method that focuses on achieving a fast adaptive binarization of QR code images through dynamic illumination equalization was proposed. First, an algorithm based on edge enhancement to obtain the position detection patterns within QR code images was applied, which enabled the acquisition of structural features in uneven illumination. Subsequently, QR code images with complex lighting conditions can achieve a fast adaptive binarization through dynamic illumination equalization. As for method validation, the experiments were performed on the two datasets that include QR code images influenced by strong light, weak light, and different shadow degrees. The results disclosed the benefits of the proposed method compared to the previous approaches; it produced superior recognition rates of 78.26–98.75% in various cases through commonly used decoders (Wechat and Zxing), with a faster processing speed of 0.0164 s/image, making it a proper method to satisfy real-time requirements in practical applications, such as a logistics sorting system.
Monitoring Gold Mining Activity Using SAR

Abstract The expansion of gold mining has had a large impact on the rainforests of the Amazon over the last decades. To take just one example, it has affected both the biodiversity and the lives of local people in the Madre de Dios region of southeastern Peru. In this chapter, we will review a methodology developed to generate early warnings of deforestation based on the use of synthetic aperture radar (SAR) images.
I want to travel to the past! The role of creative style and historical reconstructions as antecedents of informativeness in a virtual visit to a heritage tourist destination

ABSTRACTVirtual visits are a particularly useful way to learn about heritage tourism destinations. This article examines three design variables (i.e. technology interface: web browser vs. virtual reality device; creative style of information: textual vs. pictorial; and presence of historical reconstructions: yes vs. no) of a virtual visit to identify their influence on perceived informativeness and intention to recommend a destination. We performed a 2 × 2 × 2 between-subjects experimental factorial design with 116 Spanish tourists. We found that the presence of historical reconstructions especially improves informativeness when combined with extra textual information; and that if the visit is mediated by an immersive VR device, textual information cues influence intention to recommend more than do pictorial cues, but when the virtual visit is carried out using a less immersive technological interface, pictorial content has more influence on intention to recommend. Theoretical and managerial contributions are discussed.KEYWORDS: Virtual realitytourist destinationheritage tourisminformativenesseWOM AcknowledgmentsWe would like to thank the Giza Project at Harvard University for allowing us to use their actual virtual tour of the Tomb of Queen Meresankh III as a stimulus in our experimental design. We also thank Matterport for the technical support in manipulating the scenarios. As related research of this article, we want to acknowledge the conference paper Bigne, E., Ruiz, R., Curras, R. and Andreu, L. (2023). Travelling to the past. The impact of Virtual Reality on customer experience with heritage destinations. AIRSI 2023, The Metaverse Conference.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis research has been supported by the Spanish Ministry of Science and Innovation (ID grant number: PID2019-111195RB-I00/ AEI / 10.13039/501100011033 0)
Exploration and Reflection on the Construction of Embedded C Language Course

In the process of teaching embedded C language, we adopt the online and offline flipped teaching mode to design teaching cases that include both course knowledge and moral education. It can not only solve the problem of insufficient class time but also effectively combine &quot;professional theory&quot; and &quot;moral practice&quot;.
On a new method to design solar photovoltaic systems in renewable energy communities: The case of Culatra Island (Ria Formosa, Portugal)

Islands must reach sustainable lifestyles by improving resources management and by getting accustomed to renewable energy sources. Culatra, a small Portuguese island, is actively increasing renewable energy penetration into local processes in order to be the first 100% sustainable Portuguese territory by 2030. Based on the electric consumption of Culatra, the objective of the present study is to project a photovoltaic unit composed of several sub-fields, each with a distinct orientation, in order to increase the self-consumption ratio while at the same time reducing the surplus of energy production, assessed by the self-sufficiency ratio criteria, resulting in a lower levelised cost of energy of the power system. To achieve this, MATLAB’s implementation of a genetic algorithm was used to find the optimised set of orientations for a given load profile. The results indicate that it is possible to optimise the photovoltaic plant to reach a more continuous electricity generation through daytime, reducing the storage needs and increasing the value of photovoltaic systems. The method can be extended to other locations or demand curves, assisting on comparing different energy management strategies, and their respective advantages and disadvantages.
A Composite Noise Removal Network Based on Multi-domain Adaptation

Addressing the limitation of conventional single-scene image denoising algorithms in filtering mixed environmental disturbances, and recognizing the drawbacks of cascaded image enhancement algorithms, which have poor real-time performance and high computational demands, The composite weather adaptive denoising network (CWADN) is proposed. A Cascade Hourglass Feature Extraction Network is constructed with a visual attention mechanism to extract characteristics of rain, fog, and low-light noise from authentic natural images. These features are then transferred from their original real distribution domain to a synthetic distribution domain using a deep residual convolutional neural network. The generator and style encoder of the adversarial network work together to adaptively remove the transferred noise through a combination of supervised and unsupervised training, this approach achieves adaptive denoising capabilities tailored to complex natural environmental noise. Experimental results demonstrate that the proposed denoising network yields a high signal-to-noise ratio while maintaining excellent image fidelity. It effectively prevents image distortion, particularly in critical target areas. Additionally, it adapts to various types of mixed noise, making it a valuable tool for preprocessing images in advanced machine vision algorithms such as target recognition and tracking.
A Multispectral Ariel Image Stitching using Decortification and EEG Signal Extraction Technique

UAV Videos and other remote-sensing innovations have increased the demand for multispectral image stitching methods, which can gather data on a broad area by looking at different aspects of the same scene. For large-scale hyperspectral remote-sensing images, state-of-the-art techniques frequently have accumulating errors and high processing costs. However, this research paper aims to produce high-precision multispectral mapping with minimal spatial and spectral distortion. The stitching framework was created in the following manner: First, UAV collects the raw input data, which is then labeled as a signal using a connected component labeling strategy that correlates to each pixel or label using the EEG (Alpha, Beta, Theta, and Delta) technique. Next, the feature extraction process follows a novel decortication Hydrolysis CNN approach which extracts active and passive characteristics. Then after feature extraction, a novel chromatographic classification approach is employed for separating features without overfitting. Finally, a novel yield mapping georeferencing technique is employed for all images stitched together with proper alignment and segmented overlapping fields of view. The suggested deep learning model is an effective method for real-time mosaic image feature extraction which is faster by an average of 11.5 times compared to existing approaches as noted on the samples for experimental analysis.
Method for Hyperparameter Tuning of Image Classification with PyCaret

A method for hyperparameter tuning of image classification with PyCaret is proposed. The application example compares 14 classification methods and confirms that Extra Trees Classifier has the best performance among them, AUC=0.978, Recall=0.879, Precision=0.969, F1=0.912, Time=0.609 bottom. The Extra Trees Classifier produces a large number of decision trees, similar to the random forest algorithm, but with random sampling of each tree and no permutation. This creates a dataset for each tree containing unique samples, and from the ensemble set of features a certain number of features are also randomly selected for each tree. The most important and unique property of the Extra Trees Classifier is that the feature split values are chosen randomly. Instead of using Gini or entropy to split the data to compute locally optimal values, the algorithm randomly selects split values. This makes the tree diverse and uncorrelated. i.e. the diversity of each tree. Therefore, it is considered that the classification performance is better than other classification methods. Parameter tuning of Extra Trees Classifier was performed, and training performance, test performance, ROC curve, accuracy rate characteristics, etc. were evaluated.
Hybrid CNN-LSTM Network for Cyberbullying Detection on Social Networks using Textual Contents

In the face of escalating cyberbullying and its associated online activities, devising effective mechanisms for its detection remains a critical challenge. This study proposes an innovative approach, integrating Long Short-Term Memory (LSTM) networks with Convolutional Neural Networks (CNN), for the detection of cyberbullying in online textual content. The method uses LSTM to understand the temporal aspects and sequential dependencies of text, while CNN is employed to automatically and adaptively learn spatial hierarchies of features. We introduce a hybrid LSTM-CNN model which has been designed to optimize the detection of potential cyberbullying signals within large quantities of online text, through the application of advanced natural language processing (NLP) techniques. The paper reports the results from rigorous testing of this model across an extensive dataset drawn from multiple online platforms, indicative of the current digital landscape. Comparisons were made with prevailing methods for cyberbullying detection, demonstrating a substantial improvement in accuracy, precision, recall and F1-score. This research constitutes a significant step forward in developing robust tools for detecting online cyberbullying, thereby enabling proactive interventions and informed policy development. The effectiveness of the LSTM-CNN hybrid model underscores the transformative potential of leveraging artificial intelligence for social safety and cohesion in an increasingly digitized society. The potential applications and limitations of this model, alongside avenues for future research, are discussed.
DevOps Implementation Challenges in the Indonesian Public Health Organization

The importance of accelerating software development to meet rapidly changing business needs has driven the Indonesian Public Health Organization (IPHO) to adopt DevOps. But after three years, the expected benefits have not been achieved. This research aims to identify the main challenges and obstacles in implementing DevOps at IPHO. A comprehensive examination of existing literature is employed to recognize prevalent difficulties encountered by organizations when implementing DevOps. The main factors are ranked using the Fuzzy Analytic Hierarchy Process (FAHP) based on survey data from DevOps practitioners at IPHO. This study helps fill in some gaps left by empirical studies on the challenges in applying DevOps, especially in the public healthcare sector. It also streamlines the data collection and analysis process by utilizing FAHP, simplifying the survey process, and reducing the number of questions compared to previous approaches. According to the research findings, the primary hurdle that requires attention is the mindset to transform from a traditional approach to continuous delivery. In addition, the lack of understanding about the benefits of implementing DevOps and the lack of cross-functional leadership are also identified as challenges that need to be considered. However, IPHO does not view the use of legacy tools and technologies as a significant impediment to adopting DevOps.
Manajemen Kelas Dalam Meningkatkan Motivasi Belajar Siswa

The aim of the research is to describe classroom management and its impact on student learning motivation. The research method is qualitative through a case study approach. Data collection techniques using interviews, documentation, and observation. Data analysis techniques with reduction, data presentation, and conclusions. Data validity techniques using triangulation of sources and techniques. The research results show (1) classroom management: (a) planning: making semester plans, making motivational words, praying before learning, taking student attendance, and arranging seating. (b) organizing: forming a class structure and scheduling class cleaning pickets. (c) implementation: the learning process is going well. (d) controlling: asking and ensuring the learning process. (2) impact: students are happy, feel at home in class, don't joke around, and participate in learning well.
Against the norms: designing violence prevention through engaging men

Engaging with men is an essential effort towards preventing violence in our communities.At the same time, violent behaviours are deeply embedded into our societies and construct what it means to be 'masculine' in a way that naturalises it.Getting men into a space to challenge their behaviours can therefore be a challenge.In this article, we discuss the findings from a recent study with designers designing to tackle intimate partner violence.We focus on four participants' experiences of developing design interventions to engage men in violence prevention.The findings highlight strategies that engage with men in a dialectical space of critical reflection and implementing alternative behaviours.It brings key considerations for designers to think about when counteracting the normalisation of violence (or other behaviours) and community acceptance.
Integrating Project-Based Learning in Preparing Students' Interpersonal Communication Skills on Speaking Courses in Indonesia

Interpersonal communication competence plays a crucial part in the educational interaction space, as it directly impacts the behaviour of students who require a straightforward communication flow. This study aims to analyse the integration of project-based learning (PjBL) in preparing students' interpersonal communication skills in speaking courses in Indonesia, the proficiency of students' interpersonal communication skills, and the challenges of achieving them. The design of this study was a cross-sectional survey conducted in May 2023, involving 315 college students from six English Study Programmes at six universities across three provinces. Data were collected using questionnaires and interviews and were then analysed with the assistance of Jeffreys Amazing Statistics Program. The total number of interviewees was 60, with 10 from each university. The data indicate that the interpersonal communication profiles of the 315 respondents fell into the Medium category (mean = 3.26), and the most common obstacle (mean = 4.6) in achieving interpersonal communication skills on a speaking course is a difficult-to-understand speech partner's accent. This finding has implications for college students, as these interpersonal communication skills should be strengthened through PjBL and communicative competence understanding. Both micro and macro components of communication competence are essential for English learners and speakers. Syntax, morphology, semantics, and phonology are all areas where micro components perform. The three macro components also strongly contribute to the achievement of communicative competencies, including discourse competence, sociolinguistics, and communication techniques. These two components are essential for creating a more dynamic and enactive educational interaction space and are mutually reinforcing.
Developing Communication Competencies of Library Professionals Through Semiotics Theory Approach in the Contemporary Information System

Purpose: The purpose of this paper is to identify the relationship between semiotics theory and communication theory and focus on how semiotics could act as a useful tool to play a big role in the development of communication competencies of library professionals in the contemporary information system. Design/ Methodology/ Approach: The study is fundamentally analytical and qualitative in nature. The analysis of semiotic theory and communication theory is done one by one. In this paper, we use the semiotics theory approach in library services and propose an illustrative library classification list based on Dewey Decimal Classification (DDC) for the development of an effective communication competency as well as improve the visual literacy skills of library professionals. Findings: After the study, we find out that the semiotic theory is a general and unrestricted model of sign whereas communication theory is also a general and unrestricted model of communication which is useful for every field of study including the LIS field. Semiotics theory and Communication theory both are very similar and Semiotics build a strong communication competency and improve the visual literacy skills of library professionals. Practical implications: This study will be helpful for library professionals to develop their competencies according to the needs of the hour. Users will be more beneficial and attracted to the library than the formal mode of communication. Those people who cannot communicate in an unknown language and are unable to articulate language can also communicate easily through the visual mode of communication with the help of semiotics. Originality: This paper is the first to combine the theories of communication and semiotics within the context of the competency development of library professionals.
Adaptive super-resolution image reconstruction based on fractal theory

Image super-resolution (SR) reconstruction has been a significant research field in image processing, although the current approaches to dealing with complicated image reconstruction still have some issues. In order to overcome these problems, traditional approaches apply fractal geometry to image super-resolution reconstruction, either in textured or non-textured regions, treating the image as a single fractal set. However, it won't be possible to classify the image specifically, such as edge, common texture, and complex texture, unless identifying the texture complexity of all regions in the image. For the texture region, this paper considers the fractal as a perturbation function of rational interpolation and constructs a new interpolation model with scale factor parameter. The parameter is related to the local fractal dimension (LFD). This paper defines a new function between scale factor and local fractal dimension so as to select the optimal value adaptively. Considering the image edge quality, this paper combines the non-texture regions and edge regions, applies an improved edge interpolation algorithm and sets a new pixel mapping method to meet the needs of different zoom factors. Based on the above, this paper proposes an adaptive super-resolution reconstruction algorithm. Particularly, region segmentation with different texture complexity is a foundational step of our algorithm, this paper proposes an image segmentation algorithm based on the research of local fractal dimension which combines the multi-scale analysis capability of wavelets with the multi-scale self-similarity feature of fractals. The experimental results show that our algorithm can obtain accurate texture details, smooth edges and low noise subjectively, and achieve the best evaluation objectively. This paper lays the theoretical foundation for fractal applications in super-resolution image reconstruction.
PENGARUH PERMAINAN TEBAK TEBAKAN DAN KINERJA GURU TERHADAP KEMAMPUAN KOSA KATA MURID

Education is important for the future. Education is absorbed from childhood, even from the womb, to adulthood. Education lasts a lifetime, although formally, education starts from kindergarten or elementary school to university. However, the learning process in classrooms sometimes becomes less enjoyable due to several reasons, such as internal and external obstacles. To create fun learning, several techniques are feasible to apply. One of the steps that can be taken is to apply a game (in this case a guessing game). This study aims to analyze the effect of games and teacher performance on the acquisition of new vocabulary in elementary school children in Serang city. This research applies a quantitative approach by taking into account the principles of scientific research. The results of the analysis show that the 't-observation' is higher than the 't-table', namely 13.48 for 't-observation' and 2,04 for 't-table'. This study proves that guessing games can improve students' vocabulary mastery. In addition, it cannot be denied that the role of the teacher is very important. The results show that the teacher's performance when implementing the guessing game is also very high. This relates to the teacher's central role when implementing games in the classroom. Thus, the guessing game technique and teacher performance have a significant influence on students' vocabulary mastery.