Reviewerly/exHarmony huggingface dataset:
Readability of Advanced Level Turkish Textbooks Designed for Foreigners: The Case of Anahtar-Turkish Textbook

Textbooks are essential resources for both learners and teachers when teaching Turkish as a foreign language.Texts in textbooks are the most fundamental documents used to instruct foreigners in Turkish.Texts in textbooks should be appropriate for the student level in terms of a variety of characteristics.Readability is one of the characteristics cited in the literature for determining the appropriateness of textbook texts to the student level.This study analyzed the text readability levels of the Anahtar-Turkish for Foreigners C1 textbook.Document analysis was utilized in the data collection process of the study.Two different formulas were used to determine the readability of the texts in the textbook: the Ateşman and Çetinkaya-Uzun formulas.The data were descriptively analyzed.The texts were categorized according to their levels of difficulty, and the results were tabulated.The study concluded that according to both formulas, the readability levels of the texts in the Turkish for Foreigners C1 textbook are similar; the texts are at a medium level according to Ateşman and an educational level according to Çetinkaya-Uzun, and the related textbook is easier in terms of readability level than similar textbooks in the literature.
Research on multi-system interactive pop music composition based on artificial intelligence technology

Abstract The purpose of this paper is to analyze MIDI technology for pop music composition and the application of composition in artificial intelligence. Based on deep learning, a SeqVAE-based music melody generation algorithm is designed, the possibility of a pulse neural network-based music melody generation algorithm is explored, and a multi-system interactive pop music composition system is established and tested. This system's accuracy rate for bass and treble recognition was tested to be 4%, 4.1%, 2.47%, and 2.66, respectively, compared to the traditional recognition rate. The recommendation satisfaction rate was 96 when the number of queries was 50, the response speed increase time was less and kept within 3 seconds, and the CPU occupancy rate was within 40%.
Construction and Application of English Smart Classroom Teaching Model Integrating MOOC and Flipped Classroom

Abstract This paper first builds a mobile teaching platform based on the teaching mode of MOOC and flipped classroom and designs functional modules such as system management, course transmission, course design and user experience. Then, it analyzes the characteristics of students' learning behaviors using the xgboost algorithm and teaches English in innovative smart classrooms. The mobile teaching platform created for this paper is evaluated in terms of learners' examination and video-viewing behavior. From the learners’ examination behavior, the non-perfect score rate gradually declines from the highest value of 41.56% in Chapter 1 to the lowest value of 17.64% in Chapter 5, with a small fluctuation in the last chapters. This shows that the mobile teaching platform for MOOCs and flipped classrooms constructed in this paper can facilitate intelligent English classroom teaching innovation.
Designing for Ambiguity in Visual Analytics: Lessons from Risk Assessment and Prediction

Ambiguity is pervasive in the complex sensemaking domains of risk assessment and prediction but there remains little research on how to design visual analytics tools to accommodate it. We report on findings from a qualitative study based on a conceptual framework of sensemaking processes to investigate how both new visual analytics designs and existing tools, primarily data tables, support the cognitive work demanded in avalanche forecasting. While both systems yielded similar analytic outcomes we observed differences in ambiguous sensemaking and the analytic actions either afforded. Our findings challenge conventional visualization design guidance in both perceptual and interaction design, highlighting the need for data interfaces that encourage reflection, provoke alternative interpretations, and support the inherently ambiguous nature of sensemaking in this critical application. We review how different visual and interactive forms support or impede analytic processes and introduce “gisting” as a significant yet unexplored analytic action for visual analytics research. We conclude with design implications for enabling ambiguity in visual analytics tools to scaffold sensemaking in risk assessment.
A detection research of spams based on machine learning algorithms

The wide spread of spam has brought a lot of inconvenience and trouble to peoples work and lives. Therefore, it is of great practical significance to constantly update the methods of spam classification and filtering to improve the current situation of email use. In this paper, linear regression and logistic regression are examined to test whether a random email is spam or a normal email. The logistic regression model is based on a public data set that is estimated by calculating the number of entries in the entire set and then the probability of spam. The linear regression model is based on the data from the logistic regression model and is estimated to give a line representing the probability of spam in a given range of emails. Finally, the results of these two models clearly indicate the rampant and widespread nature of spam, which can enhance the publics overall awareness of carefully examining unknown emails.
Effect of the deep residual networks at different depths on expression recognition

Facial expression recognition plays a critical role in numerous applications like emotion analysis, human-computer interaction, and surveillance systems. Given the importance of this task, this study aims to investigate the effectiveness of different depths of Residual Networks (ResNet). The primary objective is to scrutinize and compare these ResNet models in terms of their training and validation losses and performance metrics like accuracy, recall, and F1 scores. In this research, a thorough comparative analysis is conducted by setting up exhaustive experiments using these models. The experiment is carried out on a popular facial expression dataset. Despite the depth differences, ResNet101 emerged as the model demonstrating superior performance. It struck the most effective balance between model complexity and generalization capacity, leading to the lowest validation loss and better performance. Experimental results show that a more complex model does not necessarily yield optimal results. The optimal balance between model complexity and generalisation needs to be investigated. These findings can provide essential guidance in the design of deep learning models for facial expression recognition and other similar tasks.
Denoising convolutional autoencoder for improving the classification performance based on noisy galaxy images

Images of galaxy objects are of great importance to the work of astronomers. Nowadays, the task of galaxy classification has been aided by Neural-Network-based classification models, who are powerful yet vulnerable to the attack of noisy images. In this research, RGB noises and bright spots simulating stars were generated, and a Convolutional-Neural-Network (CNN) based lightweight denoising image autoencoder was proposed. Firstly, a benchmark CNN classifier using DenseNet structure was trained on the Galaxy 10 DECals dataset, which consists of over 17, 000 RGB color galaxy images. Then, noisy images were generated by adding bright spots of different size and color simulating stars and applying gaussian RGB noises over the original images. The CNN autoencoder that consists of Convolutional layer in its encoder and Convolution Transpose layers in decoder was trained on the raw and noisy training data to learn effective galaxy image denoising. Finally, the effect of the autoencoder was evaluated by contrasting the performance of the CNN classifier over the noisy and denoised images. In contrast with being evaluated on the raw testing set, the CNN classifiers accuracy dropped by 0.41 when tested on the generated noisy testing images, indicating the effectiveness of the attack of image noises. While after denoising with the proposed autoencoder, the classifiers accuracy increased significantly by 0.37. Output denoised images also suggest that the autoencoder can effectively remove the applied bright spot and gaussian RGB noise, recreating the original shape of the galaxy.
Approaches on improving privacy and communication efficiency of FedFTG

The FedFTG plug-in can effectively solve the problem of knowledge forgetting caused by the server-side direct aggregation model in Federated Learning. But FedFTG runs the risk of compromising customer privacy, as well as additional transmission costs. Therefore, this paper introduces methods to enhance the privacy and communication efficiency of FedFTG, they are: Mixing Neural Network Layers method which can avoid various kinds of inference attack, Practical Secure Aggregation strategy which uses cryptography to encrypt transmitted data; The Federated Dropout model which focuses on reducing the downward communication pressure, and the Deep Gradient Compression method that can substantially compress the gradient. Experimental results show that, MixNN can ensure the privacy protection without affecting the accuracy of the model; Practical Secure Aggregation saves the communication cost when dealing with large data vector size while protecting the privacy; Federated Dropout reduces communication consumption by up to 28; DGC can compress the gradient by 600 while maintaining the same accuracy. Therefore, if these methods are used in FedFTG, its privacy and communication efficiency will be greatly improved, which will make distributed training more secure and convenient for users, and also make it easier to realize joint learning training on mobile devices.
Cat classification based on improved ResNet50

Cat species recognition holds significant potential in many fields. The primary objective of this research is to develop an automated algorithm for recognizing the presence of cats in images. The application prospects of this algorithm are diverse and include security, image search, and social media. Hence, this research has considerable practical value in various domains. In this study, we propose a cat image recognition algorithm based on the PyTorch, with ResNet50 as the foundational network architecture, and an attention mechanism (Efficient Channel Attention) integrated into the model for improved performance. We first introduced the Resnet network, and then introduced the combination of attention mechanism and Resnet in detail The proposed model achieved a 92.37% accuracy rate in classifying the 12 cat species, demonstrating its efficacy in accurately classifying and recognizing the collected images. The research conclusion of this paper has certain reference value.
A research on digital signature schemes

Currently, a lot of studies have been done on the core of Bitcoin, the blockchain. It offers a wide range of distribution mechanisms and infrastructure that keeps the data constant, unchanging, and time consistent. The blockchain is an ideal tool for assertion class applications to offer digital proof of ownership and time stamps as a result of the creation of digital summaries of physical or digital assets. So it is possible to apply the block chain to a wide range of fields, for example, online paying, trading and so on. As the block chain develops, the safety issues directly affect the effectiveness and integrity of the trade. In essence, these questions are about the safety of information. To ensure the security of the data, this paper studies and uses the security of the digital signature. The classification and characteristics of each kind of digital signature are introduced in this article, as well as some other scholars' achievements in this field are analyzed.
Pelatihan Komputer Akuntansi Aplikasi Zahir Bagi Guru Smk Se Sumatera Barat

This training is carried out in order to fulfill the tri dharma of higher education at Dharma Andalas University. The activities carried out are in the form of computer accounting training for Zahir applications for SMK teachers throughout Sumatra. cash and cash receipts and cash disbursements. The participants who took part in this training were SMK teachers throughout Sumatra who were given this training by approximately 22 teachers. The training is carried out in a tutorial and ends by giving a case. After the case is completed, an evaluation will be carried out regarding the use of the software for the teacher. After this training was held, 90% of teachers were able to understand the zahir application and the remaining 10% there were still teachers who did not understand the use of the zahir application.
Implementasi Dan Rancang Bangun Sistem Informasi Arsip Dan Tracking Inspeksi Pada BPOM Pekanbaru

At present, BPOM Pekanbaru, in the field of inspection, still uses manual methods for archiving and tracking. Therefore, employees need a long time to do archives because they are still inputted into the Microsoft Excel application. Besides that, to know the status of the submission letter, employees have to go back and forth asking and checking documents from related parties to see whether it has been followed up or not. The purpose of this research is intended for employees who have access rights to make it easier to do archives. For employee tracking activities, it is enough to check the status of the system regarding the progress of the letter and whether it has been followed up or not. The method used is a waterfall; this method is suitable for archiving and tracking problems in the inspection division because it starts from the stages of problem analysis, system design, building the system, and testing the system that has been built. The results of this study are in the form of an archive and tracking information system in the inspection division so that employees can be facilitated in archiving and monitoring documents that have been submitted.
Culture Management Literature In Realizing Student Achievement At MTsN 3 Pamekasan

Literacy culture management is an ability to manage, organize, mobilize, and control positive values ​​in carrying out thinking habits through readi ng and writing activities. Literacy culture is very supportive of the formation of students who can achieve achievement. So that if the literacy culture can be managed properly, the literacy culture that is built can realize efficient student achievements. Planning a literacy culture in realizing student achievement at MTSN 3 Pamekasan is the desire to create a literacy culture madrasa with discussions. The implementation of literacy culture in realizing student achievement at MTSN 3 Pamekasan is to be carried out every day with a predetermined time. Evaluation of literacy culture in realizing student achievement at MTSN 3 Pamekasan is by the existence of several supporting factors and inhibiting factors as well as solutions used to implement culture literacy that can realize student achievement.
THE FLEXIBILITY OF A HIGHER EDUCATION TEACHER AS AN INVARIANT OF PROFESSIONAL COMPETENCE

The article, based on methodological and theoretical analysis of scientific works, reveals the essence and significance of the personal flexibility of a higher education teacher as an invariant of their professional competence.The author tells that the personal flexibility of a higher education teacher should be considered as a mental (cognitive) ability, which characterizes the ability to reconsider the conceptual framework of the world based on acquiring new information; the ability to abandon ineffective means of interaction, modes of thinking, ways of behaving, and develop one's own, or adopt new, creative approaches to professional activities.The conditions for the formation and development of personal flexibility of a higher education teacher as an indicator of their pedagogical professionalism have been determined: improving the professional and pedagogical competence of the teacher through self-stimulation of systematic scientific, methodological, intellectual-searching cognitive activities; participation in various forms of professional development; understanding and utilization of pedagogical innovations in professional training of specialists; deepening and broadening of worldview erudition based on the formation of worldview generalizations that form the informational basis of the teacher's views on the general scientific picture of the world, types of material and spiritual systems, laws of functioning and development of nature and society, and the essence of human existence; self-reflection of internal personal mental processes: communicative behavior, motivation for readiness to adapt to new life conditions, manifestation of positive personal characteristics in interaction, perception, and control of complex professional and life situations, and the ability to generate alternative solutions.
Annotated Peruvian banknote dataset for currency recognition and classification

The real-time detection of multinational banknotes remains an ongoing research challenge within the academic community. Numerous studies have been conducted to address the need for rapid and accurate banknote recognition, counterfeit detection, and identification of damaged banknotes [1], [2], [3]. State-of-the-art techniques, such as machine learning (ML) and deep learning (DL), have supplanted traditional digital image processing methods in banknote recognition and classification. However, the success of ML or DL projects critically hinges on the size and comprehensiveness of the datasets employed. Existing datasets suffer from several limitations. Firstly, there is a notable absence of a Peruvian banknote dataset suitable for training ML or DL models. Second, the lack of annotated data with specific labels and metadata for Peruvian currency hinders the development of effective supervised learning models for banknote recognition and classification. Lastly, datasets from different regions may not align with the unique characteristics, design, and security features of Peruvian banknotes, limiting the accuracy and applicability of models in a Peruvian context [4] To address these limitations, we have meticulously curated a comprehensive dataset comprising a total of 9,315 images of Peruvian banknotes, encompassing both old and new denominations from 2011 (old) and 2019 (new) [5]. The Peruvian banknote dataset includes denominations of 10, 20, 50, and 100 Peruvian soles. Importantly, as indicated by [5], both the 2011 and 2019 families of banknotes are currently in circulation, further enhancing the dataset's relevance for real-world applications in currency recognition and verification. This dataset serves as a vital resource for addressing the challenges in real-time multinational banknote detection. By offering a comprehensive collection of images of Peruvian banknotes, both old and new, this dataset fills a critical gap in the field of banknote recognition. Researchers can utilize it to train and evaluate advanced machine learning and deep learning models, ultimately enhancing the accuracy of banknote processing systems.
Assessing the Efficiency of Machine Learning Algorithms in Detecting Cyberbullying on Twitter

Cyberbullying represents one of the unwanted behaviors which has gotten more and more common since the use of social media, particularly Twitter, has expanded quickly. Cyberbullying can have serious repercussions for its victims. Due to large volumes of data and the intricate structure of online interactions, detecting cyberbullying in real-time is an exhausting task. The purpose of this research proposal is to utilize machine learning techniques that will enhance precision and recall in recognizing instances of cyberbullying on Twitter. In the study, Twitter data is evaluated, trends and patterns are identified, and a wide range of cooperative behaviors are thoroughly assessed. The creation of a framework for collaboration, empirical analysis of patterns of collaboration, evaluation of performance indicators, and improvement of real-time detection capabilities are the main goals.
VisiAssist: “Android-based Visual Aid and Health predictor”

This innovative application is built for the visually challenged people. By using algorithms to analyze the camera feed and identify objects within the user's environment. The application be able to identify a diverse range of objects commonly found in everyday scenarios, such as furniture, obstacles, vehicles, people, and more. The detected objects are then be translated into auditory feedback, which the user can interpret and understand without needing to rely solely on visual cues. Keywords— Android application; YOLOv3 algorithm, object detection
Extrapolating a uranium map of Norway: Implications for country-scale geogenic radon risk mapping

Radon, a radioactive gas produced through the decay of uranium in the earth's crust, poses a significant health risk when it accumulates to high concentrations indoors. This study focuses on identifying areas at higher risk of radon accumulation in Norway by employing a data-driven approach based on geogenic factors, particularly the distribution of uranium on the ground surface. Utilizing two types of uranium measurements and employing a statistical methodology, we classify bedrock geology based on their average uranium content. The classification process integrates Self-organizing maps (SOM) with K-means clustering, facilitating the creation of a country-scale extrapolation. The resulting uranium map is merged from the high-resolution airborne uranium map and the extrapolated uranium map. While acknowledging the presence of uncertainties, our study offers valuable insights into geogenic radon risk, serving as a valuable resource for radon studies and mitigation efforts. Furthermore, the methodology employed in this study is characterized by its flexibility and scalability, enabling future updates and refinements to enhance radon risk assessment and management strategies.
Research on Optimization of UAVs Supertorsional Sliding Modules Based on Variable Load Applications

Unmanned logistics is leading the new trend in the development of the logistics industry. Drone delivery possesses the great economic potential for its merits of improving distribution efficiency, relieving traffic congestion, expanding coverage radius, and reducing carbon emissions, but the security of cargo drones is intensively affected by some load variations, which will lead to drone crashes and goods destruction. In this paper, an optimized continuous function is proposed to replace the most popular sign function within the sliding mode algorithm, which is integrated with a super-twist algorithm to reduce the attitude and position buffeting caused by cargo variation. The Z-axis fluctuation range of the drone-cargo joint model is reduced by 10% compared with the classical method in the simulation.