Reviewerly/exHarmony huggingface dataset:
General Forensics for Aligned Double JPEG Compression Based on the Quantization Interference

Detection of aligned double Joint Photographic Experts Group (JPEG) compressed images is a crucial area of research within the field of digital image forensics. The detection tasks for aligned double JPEG compression can be categorized into two sub-tasks, namely detecting double JPEG images with the same quantization matrix (DJSQM) or double JPEG images with different quantization matrices (DJDQM). Existing methods for one of these sub-tasks may not be effective for the other. To address this issue, a novel approach is proposed by recompressing both DJDQM and DJSQM using modified quantization coefficients. The perturbation in the recompression process results in a perturbed error image, which is valid for both DJDQM and DJSQM. Subsequently, the relative change rate is used to combine the perturbed error image, the original error image, and the quantization error to derive the interference error and the interference quantization error. The interference error and interference quantization error further expand the difference between single and double compressed images by preserving the general validity of the original image information. Furthermore, the recompression process of DJDQM and DJSQM results in the conversion of truncation and rounding errors at the pixel level, which can be represented by the pixel state map. The pixel state map characterizes the differing transformation relationships between single and double compressed images and provides additional valid features, thereby enhancing the performance of the proposed method. The empirical results demonstrate that the proposed method outperforms existing methods on detecting aligned double JPEG compressed images.
Toward Quality-aware Data Valuation in Learning Algorithms: Practices, Challenges, and Beyond

High-quality data are widely acknowledged to be instrumental in improving machine learning models. This creates the need for quality-aware data valuation (DV), whose goal is to quantify the contribution of the data during model training and thus identify high-quality and less high-quality ones. In this paper, we survey recent research efforts in quality-aware DV, with a specific focus on designs that exhibit fairness, consistency, robustness, and efficiency. These attributes enhance their practical applicability in domains like model training, data marketplaces, and emerging federated learning paradigms. Our survey encompasses an exploration of design goals and mainstream approaches in DV, as well as an examination of state-of-the-art optimization techniques. We identify and discuss potential challenges and open problems that need to be addressed further. By providing a comprehensive overview of the current state of DV research, we aim to contribute to a deeper understanding of this field.
Theoretical Exploration of Practice Teaching Reform in Preschool Education Using Social Construction Theory

The influence of social construction theory lies not only in its breakthroughs in the level of meta-theory and social theory, but also in its implications in the level of social practice. Starting from the theory of knowledge and methodology, the theory of social construction has a strong sense of criticism. Questioning the existing settings, the existing knowledge and the methodology of positivism all mean that it has the meaning of subversion and liberation in practice. Criticizing, changing or tearing up the social Settings that one does not like is an important social practice of social constructivists. In the training process of preschool education professionals, the theory of social construction has important theoretical value for the reform of practice teaching of preschool education specialty. Colleges and universities should be committed to building a community of practice teaching of preschool education specialty, creating a field of practice and experience integrating knowledge, action and thought, and promoting the transformation of teacher-student interaction from a one-man show to a duet.
Exploring the Application Effects and Experiences of Aesthetic Experiencebased Early Childhood Art Education Class: A Study on Preservice Early Childhood Teachers

The purpose of this study is to discuss the effects of aesthetic experience-based early childhood art education class and the meaning of the experience. For this purpose, we conducted a 15-week aesthetic experience-based early childhood art education class for 64 students of early childhood education at a three-year university in S city. The data from the pre- and post-quantitative tests of art teaching efficacy and aesthetic attitude were collected and paired t-test was conducted. In addition, qualitative data such as self-reflection journals, interviews, discussion results were collected and analyzed by open coding method. As a result, the preservice early childhood art education based on aesthetic experience had significant effects on art teaching efficacy, aesthetic attitude, and experience was categorized into ‘concern and burden’, ‘change and growth’.
A Study on the Effect of Career Decision Making Self-Efficacy on the Chinese College Dance Major Students’ Career Preparation Behavior: Focused on the Mediating and Moderating Effects of Social Support and Job-seeking Stress

This study aims to investigate the structural relationship between social support, self-efficacy, and job stress that affect the career preparation behavior of dance majors in order to help the difficulties faced in the process of career selection and career preparation in terms of social cognitive career theory. In this study, the relationship between variables was analyzed through multiple regression analysis through the survey method. The research findings indicated a positive influence of self-efficacy on career preparation behaviors. The mediating and moderating effects of social support and employment stress were also examined. The results revealed that self-efficacy influenced career preparation behaviors through social support. This study contributes to the development of career guidance and educational policies, providing insights to enable individuals to make more effective and satisfactory career choices.
Topic Modelling of Web Pages with Latent Dirichlet Allocation Methods

Topic modelling with Latent Dirichlet Allocation (LDA) is a popular technique used in natural language processing to uncover hidden thematic structures within a collection of documents. When applied to web pages, LDA can help in identifying prevalent topics or themes across these pages.. This study delves into the utilization of Latent Dirichlet Allocation (LDA) methods to extract underlying topics within web pages, a fundamental pursuit in understanding the multifaceted landscape of online information. Web content analysis presents unique challenges owing to its diverse nature—comprising text, images, videos, and structured HTML elements—mandating rigorous preprocessing strategies to homogenize the data. By adapting the LDA model to accommodate these challenges, this research tackles the task of uncovering latent thematic structures prevalent across web content. Methodologically, the study explores parameter tuning and model adaptation to optimize LDA for web page analysis, navigating complexities such as varied content formats, noise, and inherent biases in web data. Addressing these intricacies involves parsing HTML, extracting meaningful textual information, and refining tokenization processes. Evaluating the fidelity and interpretability of discovered topics becomes pivotal, prompting the utilization of coherence scores, perplexity metrics, and human assessment to gauge the quality of generated topics. Additionally, this research confronts the dynamic nature of web content, proposing strategies like continuous model retraining and dynamic topic modeling to accommodate evolving trends and updates. Practical applications of the extracted topics span a spectrum of domains, encompassing content recommendation systems, user behavior analysis, sentiment analysis, targeted advertising, and the enhancement of search algorithms for improved relevance and user engagement. Supported by illustrative case studies, this study elucidates how LDA serves as a potent mechanism to distill coherent and meaningful topics from web pages, offering invaluable insights into the hidden structures within the vast expanse of online information. St This comprehensive abstract encapsulates the depth and breadth of employing LDA for the analysis of web content, encompassing challenges, methodologies, evaluations, applications, and real-world implications.
Glass-to-Glass Delay Reduction: Encoding Rate Reduction vs. Video Frame Extrapolation

Applications such as teleoperated driving, remote robot control, and telepresence rely on video services to ensure real-time interaction with a satisfying quality of experience. Reducing the Glass-to-Glass (G2G) delay, i.e., the time delay between the acquisition of a video frame and its display on a remote terminal is critical for these applications. Deep learning-based video frame extrapolation before video encoding has been recently considered as an interesting solution to reduce G2G delay, however, the latency introduced by extrapolation has not been taken into account. In this paper, considering the main sources of latency, including extrapolation delay, we examine the benefits and limitations of frame extrapolation at encoder in reducing the G2G delay in a point-to-point video transmission system. To this end, we compare the latency-quality trade-off for two latency compensation methods: encoding rate reduction and video frame extrapolation. Our aim is to determine the G2G delay reduction that may be achieved at the price of a given quality reduction. Our experiments show that extrapolation methods can provide a null perceived G2G delay with an acceptable loss in quality, particularly for applications with video contents with limited temporal information. Such delay reduction is unreachable via encoding rate reduction.
YOLOv5n++: An edge-based improved YOLOv5n model to detect river floating debris

Rivers serve as vital water sources, maintain ecological equilibrium, and enhance landscapes. However, the looming issue of floating debris stemming from improper waste disposal and illegal discharge, poses an imminent threat to river ecosystems and their aesthetic appeal. Conventional human-led inspections prove labor-intensive, inefficient, and prone to errors. This study introduces an innovative approach for river debris detection, employing Unmanned Aerial Vehicles (UAVs) imagery in conjunction with a refined YOLOv5n model. This approach offers three key contributions. Primarily, the YOLOv5n model is bolstered by integrating the Efficient Channel Attention (ECA) module and reshaping the MobileNetV3 backbone to align with MobileNetV3S, thereby significantly streamlining computational demands and model intricacy. Additionally, precision and speed are augmented by eliminating the detection head for larger targets, while decreasing computational requirements. Subsequently, to counter dataset scarcity, we curate a UAV-derived river debris dataset, encompassing five prevalent debris types, serving as an indispensable resource for method refinement and assessment. Lastly, the upgraded model’s evaluation on Jetson Nano yields an mAP of 87.2%, merely 0.7% lower than the original YOLOv5n model. Remarkably, the refined model achieves substantial reductions of 57.1% in parameters, 52.6% in volume, and 54.8% in GFLOPs. Additionally, inference time is abbreviated to 57.3ms per Jetson Nano image, 13.4ms faster than the original. These findings underscore edge computing’s potential in river restoration. In conclusion, the fusion of deep learning object detection and UAV imagery empowers adept river debris detection.
QR Code Generator Application To Access Information On The Campus Building

The purpose of this research is to create and develop an application for creating QR codes that may be used to retrieve data about campus structures. Users only need to use their phone's camera to scan a QR code to obtain information immediately. Visitors to campus will find a wealth of information regarding school buildings, including location maps, a list of amenities, and emergency contacts. Java programming was used on the Android Studio platform to create this application. Building-related information is kept in the Firebase Realtime Database. A database-connected website interface allows administrators to manage data. The application for creating QR codes has been successful in producing dynamic codes with details about specific campus structures. Field testing demonstrates that the application functions properly. Buildings can have their QR codes scanned by users to view comprehensive building information. This application can be used in any section of the campus buildings to give guests immediate access to information. Tests for this study were conducted at Universitas Pembangunan Panca Budi.
Supplemental Material: Astronomical influence on Miocene continental environments in Tibet, east-central Asia

&lt;p&gt;Lithologic color rank and color chromaticity for the WHLX section in Jiuxi Basin, and lithologic color rank for the ASG section in Guide Basin.&lt;/p&gt;
The Use of Informal Affixation in Social Media Threads as Teaching Material for Indonesian Language in High School

This research focuses on informal forms of affixation on social media threads. The aims of this research are (1) to describe forms of informal affixation, (2) to describe the implementation of research results on informal affixation on social media threads as an alternative teaching material in high school. This type of research is qualitative descriptive research which aims to describe forms of informal affixation. The data in the research are all words that contain informal affixations on Threads social media. Data collection techniques use documentation, reading, listening and note-taking techniques. The results of this research show that there are two types of informal affixations on the objects studied, namely first, informal verb-forming affixations in the form of the suffix -an, suffix -in, prefix ke-, combination di-in. Second, the informal affixation that forms adjectives is in the form of the affix -an and the affix -an. Third, implementation of research data in news text teaching materials in high school with Learning Outcomes (CP) where students are able to write ideas and thoughts in news texts with clear and neat writing paying attention to appropriate punctuation.
Research of the application of blockchain and smart contract technologies in spectrum management and trading in cognitive radio networks

The rapid growth of wireless communication technologies has led to an exponential increase in the demand for spectrum resources. Spectrum management, the process of allocation and control of limited radio frequency spectrum, is critical to the efficient and equitable distribution of this scarce resource. However, traditional methods of spectrum management face many problems, including inefficiency, lack of transparency and proneness to fraud. In recent years, blockchain technology has emerged as a potential solution to these problems. The emergence of smart contracts using blockchain technology has the potential to revolutionize spectrum trading and improve spectrum management practices. This paper explores the application of blockchain in spectrum management and discusses its potential to revolutionize the way spectrum resources are allocated and managed. Also, the concept of smart contracts and their application in spectrum trading are studied. It discusses the benefits of using smart contracts for transparent and efficient spectrum transactions, implementation challenges, and potential solutions. Through the use of smart contracts, a secure and decentralized market for spectrum resources can be created, facilitating dynamic and flexible allocation of spectrum.
THE ROLE OF THE AXIOLOGICAL APPROACH IN THE SYSTEM OF TRAINING CADETS OF UNIVERSITIES OF THE MINISTRY OF EMERGENCY SITUATIONS OF RUSSIA

This article discusses the main aspects of the system of training cadets of universities of the Ministry of Emergency Situations of Russia for professional activity. The role of the axiological approach in the construction of the training system, the value orientations of cadets in the learning process and the results that contribute to the formation of the necessary competencies in them are shown.
Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk

In today's daily life we are getting so many anonymous calls. Some calls are related to loan marketing and finance. As per the survey, one person is getting 26% spam calls in a day. The proposed methodology accepts user calls and based on the conversation the spam numbers are identified and the same information is provided to the other callers. This is possible because of machine learning-based sentiment analysis. Sentiment analysis is the subdomain of machine learning. The goal of this research is to propose an adaptive methodology for incoming calls. The sentiment-based incoming calls help desk works with freely available lexical resources WordNet, SemCor, and OMSTI. The discussed methodology accepts user conversations in audio format the speech-to-text conversion of the audio will be done. After pre-processing the keyword is detected from the statement. The word2Vec word embedding technique is used for representing words from document space to vector space. The 150-200 dimensional word vector is generated. The WordNet is used for sense mapping and keyword identification. Based on the sentiment analysis of input calls the decision is taken whether to accept or reject calls. This methodology is generating superior results for supervised machine learning models.
Efektivitas Penggunaan SYAM-OK Sebagai LMS Dalam Mendukung Proses Pembelajaran Daring di Prodi Pendidikan Teknik Informatika Dan Komputer Universitas Negeri Makassar

This study aims to determine the level of effectiveness of using the SYAM-OK LMS in online learning in the Informatics and Computer Engineering Education Study Program. This study used a quantitative approach with a descriptive research type with a total sample of 254 for 2018-2020 class student subjects and 19 samples for lecturer subjects in the Informatics and Computer Engineering Education Study Program, Faculty of Engineering, Makassar State University. The research data were obtained from questionnaires and documentation, while the data analysis technique used was descriptive statistical analysis. Based on the discussion and results of the research that has been done, the use of the SYAM-OK LMS is said to be effective, where the results show that the level of effectiveness of using the SYAM-OK LMS in online learning for students is in the high category with a percentage of 75% in both functional aspects, program aspects, aspects of goals and aspects of ideal conditions, while the level of use of LMS SYAM-OK by lecturers is also in the high category with a percentage of 58%. Thus, the use of the SYAM-OK LMS for students and lecturers in the Informatics and Computer Engineering Education Study Program, Makassar State University has been effective
Beyond top-k: knowledge reasoning for multi-answer temporal questions based on revalidation framework

Answer sorting and filtering are two closely related steps for determining the answer to a question. Answer sorting is designed to produce an ordered list of scores based on Top-k and contextual criteria. Answer filtering optimizes the selection according to other criteria, such as the range of time constraints the user expects. However, the unclear number of answers and time constraints, as well as the high score of false positive results, indicate that the traditional sorting and selection methods cannot guarantee the quality of answers to multi-answer questions. Therefore, this study proposes MATQA, a component based on multi-answer temporal question reasoning, using a re-validation framework to convert the Top-k answer list output by the QA system into a clear number of answer combinations, and a new multi-answer based evaluation index is proposed for this output form. First, the highly correlated subgraph is selected by calculating the scores of the boot node and the related fact node. Second, the subgraph attention inference module is introduced to determine the initial answer with the highest probability. Finally, the alternative answers are clustered at the semantic level and the time constraint level. Meanwhile, the candidate answers with similar types and high scores but do not satisfy the semantic constraints or the time constraints are eliminated to ensure the number and accuracy of final answers. Experiments on the multi-answer TimeQuestions dataset demonstrate the effectiveness of the answer combinations output by MATQA.
Multiple improvement strategies for high-density text detection based on DBNet

High-Density text detection is actually challenging task, which deserves more attention on its performance improvement. Based on a typical existing text detection model called DBNet, this paper introduces a new approach in improving the performance of it in text-rich and readability interfered scenarios, which are mostly characterized by dozens of lines of text using different font sizes and text background harder for detecting and reading, the most significant scenario is text detection on the passport information page. The approach proposed in this study includes several steps, namely a baseline setting, a method of dataset enrichment and new loss function aiming at raising text detection for short texts and single characters, such methods has achieved enhanced improvement on text detection especially on short texts: an overall precision reaching 0.7854 and recall reaching 0.8758. Compared with the base model, the experimental results demonstrated the effectiveness of the proposed methods in this study.
Combine model fine-tuning freezing layers and adaptive filter modulation to implement transfer learning for GANs

Generative Adversarial Network (GAN) requires more resources to train than other deep learning models and its loss function converges more slowly. For this reason, scholars at home and abroad have proposed a GANS algorithm based on transfer learning, which is applied to fewer samples, thus improving the training effect of GANS algorithm. In this paper, we provide a new way to perform the transfer of genetic algorithms and combine the two ways. On this basis, we will compare and analyze a variety of transfer learning algorithms to verify the feasibility and effectiveness of the joint application.