Reviewerly/exHarmony huggingface dataset:
UAV target detection algorithm based on improved YOLOv5s

Real-time UAV monitoring is an important means of battlefield reconnaissance, and machine interpretation of UAV images has become the main form of image interpretation, so the merit of the algorithm becomes an important factor limiting UAV reconnaissance. To address the problems of insufficient graphics card arithmetic power, low detection accuracy and difficult deployment of algorithm models at the embedded end of UAVs, this paper proposes an improved lightweight target detection algorithm based on YOLOv5s, adding K-means++ algorithm and CA attention mechanism module to the original algorithm, and training the improved YOLOv5s-CA network using tank dataset, and the simulation results show that: the improved YOLOv5s-CA has an mAP value of 97.50%, an F1 value of 0.96, and an FPS value of 74.8, which can be deployed on UAVs for real-time detection.
Hybrid attention network for semantic segmentation

Deep neural networks have made significant improvements in pixel-level semantic segmentation. However, the existing semantic segmentation algorithm still faces the problem of weighing between the accuracy and calculation cost of the segmentation. In response to this issue, this article proposes a hybrid network structure (HAM), looking for a balance point in the calculation accuracy and calculation speed. In this method, we construct a dual-attention module. The role of this module is to guide high -level characteristics through underlying characteristics to obtain more context information. Among them, the shape flow branch retains low-level space details, and semantic flow branches capture senior context information. These two branches are fused to strengthen information dissemination between different levels, thereby achieving higher segmentation accuracy. Experiments on the dataset show that this method achieves higher accuracy and speed under relatively small parameters. Compared with other real-time semantic segmentation methods, our network has achieved good compromise between parameters, speed, and accuracy.
Peer Review Statement

All papers published in this volume have been reviewed through processes administered by the Editors. Reviews were conducted by expert referees to the professional and scientific standards expected of a proceedings journal published by IOP Publishing. • Type of peer review : Double Anonymous • Conference submission management system : Morressier • Number of submissions received : 96 • Number of submissions sent for review : 95 • Number of submissions accepted : 74 • Acceptance Rate (Submissions Accepted / Submissions Received × 100) : 96.9 • Average number of reviews per paper : 2 • Total number of reviewers involved : 9 • Contact person for queries: Name : Kadhung Prayoga Email : kadhungprayoga@gmail.com Affiliation : Universitas Diponegoro
The application of modern virtual reality technology in the teaching of vocal music

The application of modern virtual reality technology in various fields is changing day by day, and its application in the field of music is also increasing. And in the music branch of vocal music teaching, the application of these modern science and technology seems more urgent. Like three-dimensional virtual technology, virtual reality technology (VR), augmented reality technology (AR) of these modern technologies, if it can be combined with vocal music teaching, it will bring revolutionary changes for vocal music teaching. Three-dimensional virtual reality technology can turn the abstract acoustic principles of vocal teaching, vocal musculature and music performance virtual simulation into intuitive and realistic scenes, providing students with rich learning opportunities and immersive experiences to improve vocal singing skills and enrich the emotional experience, so as to enhance the quality of vocal teaching and teaching effect.
Exploration and Application of Artificial Intelligence—The Case of Oral English and English Writing

In order to improve the traditional oral English teaching and English Writing abilities of learners in China, the study employs artificial intelligence, which revolutionizes the traditional oral English and English writing teaching methods. Based on artificial intelligence, the teachers effectively improve their teaching efficiency, and the learners cultivate their abilities. The teaching practices in oral English and English writing show that the employment of artificial intelligence can effectively improve the learners’ oral English and English writing abilities.
Tessellating the Latent Space for Non-Adversarial Generative Auto-Encoders

Non-adversarial generative models are relatively easy to train and have less mode collapse than adversarial models. However, they are not very accurate in approximating the target distribution in latent space because they don't have a discriminator. To this end, we develop a novel divide-and-conquer model called Tessellated Wasserstein Auto-Encoders (TWAE) which has less statistical error in approximating the target distribution. TWAE tessellates the support of the target distribution into a given number of regions using the centroidal Voronoi tessellation (CVT) technique and designs data batches according to the tessellation instead of random shuffling for accurate computation of discrepancy. Theoretically, we demonstrate that the error in estimating the discrepancy decreases as the number of samples <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$n$</tex-math></inline-formula> and the regions <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$m$</tex-math></inline-formula> of the tessellation increase at rates of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{n}})$</tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(\frac{1}{\sqrt{m}})$</tex-math></inline-formula> , respectively. TWAE is very flexible to different non-adversarial metrics and can significantly enhance their generative performance in terms of Fréchet inception distance (FID) compared to existing ones. Furthermore, numerical results demonstrate that TWAE is competitive to the adversarial model and shows powerful generative ability.
Machine Learning Method-Based Static Infrared Gesture Recognition System

Infrared sensor arrays are extensively utilized in applica-tions such as human activity monitoring and gesture recognition systems. The high-density configuration of these sensor arrays ensures a reliable and consistent platform for uncontact signal detection. This letter presents a machine-learning approach for infrared gesture recognition. The infrared sensor array employed in this study comprises 16 readout channels, each equipped with a corresponding voltage amplification unit. The sensors are spaced 3 cm apart, while the voltage amplifier consumes 4.77 W. A back propagation (BP) neural network is used to process and classify gesture data. To improve accuracy, the weights of the BP neural network are initialized by using a genetic algorithm. Experiments show that infrared gesture signals can be classified effectively by the proposed modified BP neural network, which achieves an accuracy of over 96% on the test set.
A Novel Unified Framework for Multi-Task Fusion of Hyperspectral and SAR Image

Classification using multi-source remote sensing data has attracted widespread attention and is playing an increasingly important role in various fields. However, due to the disparity in imaging methods and the informational imbalance among data from various sources, it is still challenging to incorporate complementary advantages. Furthermore, real-world situations frequently involve varied data resolution, and the outcomes of straightforward super-resolution preprocessing are not always helpful for performing subsequent classification tasks. In this paper, we propose a unified framework for joint super-resolution and classification tasks of low resolution hyperspectral images (LR-HSIs) and SAR images. The minimization of the proposed joint loss function in the generative adversarial network (GAN) framework, including super-resolution and classification objective functions, can effectively achieve the multi-task goals. Experimental results on the super-resolution and classification of real LR-HSIs and SAR images demonstrate the effectiveness of the proposed method both visually and quantitatively.
Digital age literacy profile of high school students and its implementation in biology learning

This study aims to describe high school students’ digital-age literacy skills. This study is a descriptive study with a quantitative approach. The sample of this study was 322 eleventh-graders of science at SMA Negeri in Pontianak City, which were determined using the cluster sampling technique. The instrument used is a questionnaire developed based on eight domains of digital-age literacy. The results showed that students' level of digital-age literacy was included in the medium category. Based on the domain, technological literacy, multicultural literacy, and global awareness were in the high category, while basic literacy, information literacy, scientific literacy, visual literacy, and economic literacy were in the medium category. The implementation of digital-age literacy in biology learning is expected to create more engaging and effective biology learning and improve students' digital-age literacy skills.
Investigating the Attitudes of ESL Learners toward the English Language

The study aims to understand the attitude of undergraduate ESL learners toward English in the Mewat region of Haryana. It is also concerned with investigating how differences in demography govern ESL learners' learning patterns and habits. For this purpose, 400 students (which makes 10%) of the total population of 4000 students in various colleges of Mewat region were selected randomly to understand the patterns and their behaviour toward learning English. To carry out the research, a questionnaire was developed to gather data and collect the responses and attitudes of the ESL learners. The quantitative analysis was employed with the help of descriptive statistics, and one-way ANOVA. The analysis demonstrated that most of the participants have a high degree of motivation to learn English. It also suggested that there is no significant difference in their attitudes based on family income, the education level of their parents, and gender. As positive attitudes act as an important means of motivation for ESL learning, these findings will be of immense help to teachers to reinvent their teaching strategies and design their study material accordingly.
ALGORITMOS SUPERVISADOS PARA LA PREDICCIÓN DEL ANCHO DE BANDA DE LAS APLICACIONES EN AMAZON WEB SERVICE DESDE UNA PYME RURAL

This article presents a methodology to measure the bandwidth behaviour by making predictions of the network traffic that connects to the cloud in small and medium enterprises in rural areas with difficult access in Colombia, in order to optimize network resources over time and ensure the quality of service in web applications. A comparative study of three neural network algorithms that model a multilayer neural network is performed, selecting the one that has a minimum error that approaches zero; the selected algorithm is trained from a data source to predict the network traffic that connects to the cloud.It is necessary to analyse network behaviour to ensure the quality of web applications in the cloud that transmit information such as data, images, sound, video, etc., some in real time, and that generate large volumes of traffic. Understanding the traffic flowing through the network enables network capacity planning when managing limited resources, such as in the case of small and medium-sized enterprises in rural areas. As a product of the research analysis, a free software prototype will be developed to perform the measurements and predictions in rural areas. The results of the implementation show that the proposed approach is superior to other forecasting methods in terms of accuracy and predictability.
Reduced-Complexity Multirate Remote Sensing Data Compression With Neural Networks

One of the main limitations to the adoption of deep learning for image compression is the need to train multiple models to compress at multiple rates. In the case of onboard remote sensing data compression, another limitation is the computational cost of the neural networks. Addressing both limitations, this paper presents a new reduced-complexity architecture for multi-rate compression of remote sensing images. The proposed architecture enables compressing at a precise user-selected rate while keeping a competitive performance in lossy compression on different sets of remote sensing data. The proposed approach is amenable for onboard deployment.
Unleashing the Power of Shared Label Structures for Human Activity Recognition

Current human activity recognition (HAR) techniques regard activity labels as integer class IDs without explicitly modeling the semantics of class labels. We observe that different activity names often have shared structures. For example, "open door" and "open fridge" both have "open" as the action; "kicking soccer ball" and "playing tennis ball" both have "ball" as the object. Such shared structures in label names can be translated to the similarity in sensory data and modeling common structures would help uncover knowledge across different activities, especially for activities with limited samples. In this paper, we propose SHARE, a HAR framework that takes into account shared structures of label names for different activities. To exploit the shared structures, SHARE comprises an encoder for extracting features from input sensory time series and a decoder for generating label names as a token sequence. We also propose three label augmentation techniques to help the model more effectively capture semantic structures across activities, including a basic token-level augmentation, and two enhanced embedding-level and sequence-level augmentations utilizing the capabilities of pre-trained models. SHARE outperforms state-of-the-art HAR models in extensive experiments on seven HAR benchmark datasets. We also evaluate in few-shot learning and label imbalance settings and observe even more significant performance gap.
Graph Exploration Matters: Improving both Individual-Level and System-Level Diversity in WeChat Feed Recommendation

There are roughly three stages in real industrial recommendation systems, candidates generation (retrieval), ranking and reranking. Both individual-level diversity and system-level diversity are important in this framework. The former focus on each single user's experience, while the latter focus on the difference among users. Graph-based retrieval strategies are inevitably hijacked by heavy users and popular items, leading to similar results for each user and the convergence among users. In the reranking phase, Determinantal Point Process (DPP) is widely deployed to increase individual-level diversity. Heavily relying on the semantic information of items, DPP suffers from clickbait and inaccurate categories. Besides, most studies only focus on one of the two levels of diversity, and ignore the mutual influence among different stages in real recommender systems. We argue that individual-level diversity and system-level diversity should be viewed as an integrated problem, and we provide an efficient and deployable solution for web-scale recommenders. Generally, we propose to employ the retrieval graph information in diversity-based reranking, by which to weaken the hidden similarity of items exposed to users, and consequently gain more graph explorations to improve the system-level diversity. Besides, we argue that users' propensity for diversity changes over time in content feed recommendation. Therefore, with the explored graph, we also propose to capture the user's real-time personalized propensity to the diversity. We implement and deploy the combined system in WeChat App's Top Stories used by hundreds of millions of users. Comparative and long-term online A/B tests show our solution can effectively improve both user engagement and system revenue.
PENGARUH DISCOVERY LEARNING DAN YOUTUBE TERHADAP KEMAMPUAN MENULIS CERPEN SISWA KELAS IX SMPN 4 SUKABUMI

This study aims to describe the use of discovery learning and YouTube on the ability to write short stories in class IX SMPN 4 Kota Sukabumi. This study used an experimental method with a one-group pretest-posttest research design. The sample in this study was 26 students, using the purposive sampling technique. The data collection technique was in the form of an essay test that was conducted twice, namely a pretest and a posttest. Based on the results of the analysis, there are several problems that cause students to have difficulty writing short story texts, namely difficulty developing ideas from the given theme, lack of vocabulary mastery, lack of student motivation in writing, and the use of learning models and media that are given less creatively. The results showed that the average score of students when doing the pretest was 53.31, which increased to 76.38 in the posttest results. The calculation results of the test were obtained using the t-test, obtained a t_hitung value of 15.072 and a sig value (2-tailed) of 0.00. Thus, the sig value (2-tailed) &lt;0.05 which indicates that there is a significant difference between the pretest and posttest. Thus, it can be concluded that the use of the discovery learning model and youtube has an effect on the ability of students in class IX SMP Negeri 4 Kota Sukabumi to write short stories.
Hubungan Tingkat Literasi Ibu pada Balita terhadap Keaktifan Kunjungan Posyandu

Introduction: Posyandu is one of the health service programs provided by the local village so as to make it easier for the community to check health, especially for pregnant women and toddlers, therefore the activeness of mothers and tollers is very important in this activity so that toddler nutrition is monitored and the health of pregnant women is monitored. Objectives: To determine the relationship between maternal literacy level in toddlers to the activeness of posyandu visits Method: The type of research used is quantitative, this research uses an analytical survey design using a cross-sectional approach. The researcher used a total sampling technique to determine the sample, so a sample of 120 respondents was obtained. This research used a literacy level questionnaire (HLS-EU-Q) directly using a door-to-door mechanism and assisted by local posyandu cadres. Data analysis used the chi-square test. Result: Based on the research results, it was obtained from the chi-square test of the relationship between a mother's literacy level in toddlers and the activeness of Posyandu visits, a p-value of 0.316 or p &gt; 0.050. Conclusion: There is no relationship between the literacy level of mothers of toddlers and their activeness in visiting Posyandu Nusa Indah RW 03 Cigombong Village, Bogor Regency. This happens because mothers use the internet to search for information about toddlers' health rather than getting information directly through the posyandu.
EduSign: Real-Time Application for Spanish Sign Language Recognition

According to a survey by the National Institute of Statistics in Spain, around 2.2% of the hearing impaired population, equivalent to 27 300 people, use Sign Language to communicate. This highlights the importance of ensuring accessibility to Sign Language as a fundamental resource to guarantee inclusion and equal opportunities for deaf and hearing impaired people. To achieve this goal, it is essential to promote and disseminate Sign Language and to remove barriers to its usage and access. Technology can be of great help in this field. Therefore, this work presents EduSign, a new AI-based application that classifies the alphabet of the Spanish Sign Language in real time aiming at facilitating the communication of people with hearing disabilities. Specifically, EduSign is a web application that runs a machine learning algorithm (i.e., a neural network) where users can learn and practice the Spanish Sign Language alphabet. The methodology that enabled the creation of EduSign consisted on capturing the coordinates of the gestures made in Sign Language using the MediaPipe tool. These coordinates were then used to train several machine learning classifiers (i.e., K-Nearest Neighbors, Ridge Regression, Gradient Boosting, Random Forest, Logistic Regression and a Neural Network). Out of these classifiers, the neural network offered the best performance. More specifically, using a custom dataset with the complete Spanish alphabet, it shown an accuracy of 99%. These results reinforce the idea that the application of artificial intelligence techniques can be of great help in improving communication for deaf and hearing impaired people. Hence, it is hoped that the system can be used effectively by the target audience.
Compliance and Audit Challenges in DevOps: A Security Perspective

DevOps has revolutionized software development by enabling faster delivery and increased collaboration.However, this rapid pace introduces security concerns that demand attention.This journal article explores the compliance and audit challenges faced in DevOps from a security perspective.It emphasizes the significance of integrating security into DevOps practices and the importance of compliance in dynamic environments.The article also outlines key security considerations, including threat identification, secure coding practices, and automating compliance checks.Furthermore, DevOps organizations must navigate compliance frameworks.Common challenges include vulnerability management, identity and access management, and data security.Strategies for addressing these challenges involve automation, integrating compliance requirements into the DevOps pipeline, and fostering a culture of compliance.Auditing DevOps environments ensures security and compliance.The future of DevOps security and compliance lies in emerging technologies like AI and ML, navigating regulatory changes, and securing cloud-native environments.This article serves as a wake-up call for organizations to prioritize security and compliance in DevOps.It emphasizes the need for collaboration between development, operations, and compliance teams and the continual improvement of security practices.
A Parallel Framework for Streaming Dimensionality Reduction

The visualization of streaming high-dimensional data often needs to consider the speed in dimensionality reduction algorithms, the quality of visualized data patterns, and the stability of view graphs that usually change over time with new data. Existing methods of streaming high-dimensional data visualization primarily line up essential modules in a serial manner and often face challenges in satisfying all these design considerations. In this research, we propose a novel parallel framework for streaming high-dimensional data visualization to achieve high data processing speed, high quality in data patterns, and good stability in visual presentations. This framework arranges all essential modules in parallel to mitigate the delays caused by module waiting in serial setups. In addition, to facilitate the parallel pipeline, we redesign these modules with a parametric non-linear embedding method for new data embedding, an incremental learning method for online embedding function updating, and a hybrid strategy for optimized embedding updating. We also improve the coordination mechanism among these modules. Our experiments show that our method has advantages in embedding speed, quality, and stability over other existing methods to visualize streaming high-dimensional data.