Reviewerly/exHarmony huggingface dataset:
Enhancing Monocular Depth Estimation Using Attention in Hybrid Network

Within the realm of deep learning-based Monocular Depth Estimation (MDE), Vision Transformers (ViTs) have garnered substantial attention as a network framework owing to their unique structure and impressive attention mechanism. However, ViTs encounter limitations in effectively capturing spatial features and exhibit a lack of sensitivity towards local information when compared to Convolutional Neural Networks (CNNs). This study aims to address the underutilization of valuable local information by ViTs and the oversight of performance improvement facilitated by the decoder. To tackle these challenges, we propose a hybrid network that leverages the strengths of both ViTs and CNNs to enhance both local information and long-range dependencies. In the encoder stage, we introduce an innovative patch attention mechanism to capture varying levels of attention across diverse regions. Furthermore, in the decoder stage, a cross-attention mechanism is devised to enhance feature fusion. Through extensive experimentation on diverse datasets, including KITTI, DIW, DIODE, and Sintel, our approach achieves more effective and representative features, leading to significant performance improvements of up to 13.98% compared to the state-of-the-art benchmarks in the MDE task.
VT-Scanner: Layout Similarity on Smartphone and Its Application for Robust Scene Recognition

Mobile applications are becoming attractive targets for revealing usage habits and trends. However, most research studies mainly focus on network traffic classification, which corresponds only to the characteristics of data transfer. Besides, encrypted traffic has limited the attention to robustness as well as intrinsic relationships between apps. In this paper, we introduce VT-Scanner, a robust method for recognizing usage scenarios from user interface layouts. Unlike existing works on UI representation, we first construct a histogram feature for View Tree to explore cross-app similarity with interpretability. We further employ a tree-fitting encoder Tree-LSTM, incorporating multi-view prior knowledge for reconstruction mechanism. VT-Scanner learns a more general representation from View Tree than pixel or layout visualization. Our experiments on scene recognition and app recognition show the high robustness of VT-Scanner to zero-day applications, version differences, and device variations, while ensuring privacy.
Nonbinary Zipper Codes Based on Reed-Solomon Codes

In this paper, we study nonbinary zipper codes constructed based on Reed-Solomon (RS) codes. In addition, the sliding window decoding (SWD) based on adaptive belief propagation (ABP) is proposed. Furthermore, we propose the miscorrection mitigation algorithm by introducing a scaling factor β. Simulation results show that a rate 0.73 zipper code constructed by (63,55) RS code, using SWD based on ABP, has better performance than the SWD based on hard-decision decoding by 2.2 dB at a frame error rate (FER) of 10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">−6</sup> .
DNAT: Multi-scale Transformer with Dilated Neighborhood Attention for Image Classification

Recently, Transformer-based models have achieved remarkable progress for image classification. In order to reduce the computational costs of multi-head self-attention (MHSA) that is widely adopted in recently-proposed visual Transformers, some window-based Transformers employ local self-attention via shrinking number of input tokens. Nevertheless, these approaches to some extent are short to model long-range dependencies. To address this problem, this paper presents an effective and powerful Transformer backbone, called Dilated Neighborhood Attention Transformer (DNAT) that has a mutli-path architecture, for image classification. DNAT is able to explore long-range interactions through dilated neighborhood attention (DNA). When dilation rate is small, DNAT degenerates to local self-attention that is computationally efficient to capture local features. As the dilation rate increases, more and more long-range token interactions are explored step-by-step. Finally, by integrating weighted features derived from various dilation rates, DNAT seamlessly harvests local-to-global context, yet with acceptable growth of model size and computational budgets. The extensive experiments on ImageNet-1K and CIFAR100 datasets demonstrate the effectiveness of DNAT for image classification.
Adaptive Bitrate Video Semantic Communication over Wireless Networks

This paper investigates the adaptive bitrate (ABR) video semantic communication over wireless networks. In the considered model, video sensing devices must transmit video semantic information to an edge server, to facilitate ubiquitous video sensing services such as road environment monitoring at the edge server in autonomous driving scenario. However, due to the varying wireless network conditions, it is challenging to guarantee both low transmission delay and high semantic accuracy at the same time if devices continuously transmit a fixed bitrate video semantic information. To address this challenge, we develop an adaptive bitrate video semantic communication (ABRVSC) system, in which devices adaptively adjust the bitrate of video semantic information according to network conditions. Specifically, we first define the quality of experience (QoE) for video semantic communication. Subsequently, a swin transformer-based semantic codec is proposed to extract semantic information with considering the influence of QoE. Then, we propose an Actor-Critic based ABR algorithm for the semantic codec to enhance the robustness of the proposed ABRVSC scheme against network variations. Simulation results demonstrate that at low bitrates, the mean intersection over union (MIoU) of the proposed ABRVSC scheme is nearly twice that of the traditional scheme. Moreover, the proposed ABRVSC scheme, which increases the QoE in video semantic communication by 36.57%, exhibits more robustness against network variations compared to both the fixed bitrate schemes and traditional ABR schemes.
Diffusion-Generated Fake Face Detection by Exploring Wavelet Domain Forgery Clues

Recently, diffusion models have shown remarkable success in generating high-quality images, making them potentially more difficult t o d etect t han G AN-generated i mages. In this paper, we address the emerging challenge of detecting face forgeries generated by diffusion models. We leverage insights from frequency artifacts in GAN-generated images and delve into the frequency domain characteristics of diffusion-generated images using both discrete Fourier transform (DFT) and Haar discrete wavelet transform (HDWT). Our investigation reveals that, while diffusion-generated images lack obvious DFT artifacts like those in GAN-generated images, they exhibit fewer high-frequency details compared to real images. Building upon these observations, our multi-scale network incorporates wavelet lifting and wavelet-spatial transformer blocks, enabling precise frequency decomposition and efficient f eature f usion. Experimental results on two generated datasets demonstrate the superior robustness of our proposed method compared to state-of-the-art forgery detection methods, making it an effective solution for identifying face forgeries produced by diffusion models. The dataset and code are open source at https://github.com/eecoder-dyf/Diffusion-Detection-WCSP2023.
Identity-aware Pseudo-Supervision Face Image Super-resolution for Surveillance Scenarios

Generic super-resolution models adopt a predefined degradation kernel (e.g., Gaussian) or estimate the degradation kernel from low-resolution images to downsample high-resolution images as the training data, because there are few low-resolution and high-resolution image pairs. However, the low-resolution images captured in surveillance scenarios are severely impacted by capture distance, illumination, occlusion, etc., so the degradation kernel is too complicated to predefine or estimate. Meanwhile, identity preservation is significant for face image super-resolution. To this end, we propose identity-aware pseudo-supervision face image super-resolution in real-world scenarios, especially surveillance scenarios. We utilize Generative Adversarial Nets (GAN) to degenerate high-resolution images to follow the distribution of low-resolution images captured by surveillance cameras. The synthesized low-resolution images and corresponding high-resolution compose paired training data with pseudo-supervision. Furthermore, to preserve the identity during the degeneration, a face image quality assessment module is plugged into the degeneration module, which balances the degradation and identity preservation. We conduct extensive experiments on multiple benchmarks to verify the effectiveness of the proposed method.
Sistem Pendeteksi Kesegaran Susu Formula Bayi Berbasis Internet Of Things

This research was conducted to design a tool for detecting the suitability of baby formula milk based on the internet of things. This tool is designed to obtain valid data on the period of time when baby formula milk is still suitable for consumption or is no longer suitable for consumption. As is known, the duration of milk spoilage cannot be measured with certainty. There are several sensors used in this system, namely a pH sensor and a gas sensor as well as reading detection results using an LCD and sending them in real time to Telegram. The microcontroller used is ESP32. Based on data obtained from testing using a pH sensor and a gas sensor, the results obtained were that the pH sensor detected that the brewed baby formula milk was considered unfit for consumption after 4 hours in free air conditions due to the results of the pH sensor showing, namely 6.44, which is the value indicates the milk has become acidic. Meanwhile, for the gas sensor, there was a significant change in value from the initial value after 5 hours of the milk being left in free air. The pH sensor was a fairly good detection tool in detecting spoilage of baby formula milk because the MAPE obtained was 1.34%.
MIMO Detection Based on Graph Neural Network with Belief Propagation

Graph neural network (GNN) has been considered for massive multiple-inputmultiple-output (MIMO) detection recently due to its strong capability in solving graph-based classification problems. However, current GNN detectors still struggle between performance and complexity under high order modulations. In this paper, we propose a novel GNN-based MIMO detector named GNN-BP, which combines GNN and belief propagation (BP) messages. The GNN is constructed based on the Markov random field (MRF) MIMO model, while extra node features are introduced from BP as a supplement to effectively enhance the detection performance with acceptable extra cost. Simulation results show that the proposed GNN-BP detector can outperform state-of-the-art (SOA) GNN-based detectors, and achieves similar performance as GNN with expectation propagation (GEP) with much lower computational cost.
INFOGRAFIS KEPENDUDUKAN KOTA MAKASSAR MENGGUNAKAN ALGORITMA K-MEANS

Makassar City is the fourth-largest city in Indonesia and the largest in Eastern Indonesia with an area of 175.77 square km and a population of Â±1.5 people in 2019, divided into 15 districts and 143 sub-districts. Uneven population density in each sub-district has an impact on the emergence of social problems in society such as: unhealthy environment, chaotic building management, population registration information that is not updated. To overcome this problem, it is necessary to have population info graphics that show the population, age of the population, education, etc. With info graphic data on the population of Makassar city that can be accessed online, people looking for data can use it as a reference. To achieve the objectives of this research, the K-means algorithm method is used to divide observations into K clusters where each observation is a member of the cluster with the closest average value, through an iterative process until the grouping converges. The K-means algorithm can be used on population data. For example, the grouping of population data is divided into three clusters, namely (cluster 1) dense, (cluster 2) medium and (cluster 3) not dense. The results obtained show that five districts: Mariso, Mamajang, Makassar, Bontoala and Tallo are the districts with the densest populations.
Analisis Sistem Security Information and Event Management (SIEM) Aplikasi Wazuh pada Dinas Komunikasi Informatika Statistik dan Persandian Sulawesi Selatan

The South Sulawesi Communication Informatics Statistics and Standardization Office is an implementer of government affairs that assists in carrying out government affairs in the fields of communication, informatics, statistics, and signage. Currently, agencies are utilizing technological developments to maximize their performance, such as the use of web servers to provide information and provide services. But of course this can cause problems such as data theft. Because of the many threats that can attack at any time. Therefore, an application is needed that can prevent this from happening. In order to overcome this, a monitoring system is implemented using the Wazuh application which is an application of SIEM. to find out how this application works in the event of an attack, testing will be carried out using 2 types of attacks, namely Distributed Denial of Service (DDoS) Slowloris and Brute Force. In this test, data will be taken in the form of application responses, namely the response time of the Wazuh application and the classification of the Wazuh application level against DDoS and Brute Force attacks which will be displayed on the Wazuh application Dashboard. Based on the test results, the wazuh application successfully detects DDoS Slowloris and Brute force attacks and can classify these two attacks at levels 3 to 10.
Characterization of Statistically Forced Oscillations : A Spectral Proper Orthogonal Decomposition Approach

In this paper, a method for statistical analysis is developed to estimate patterns of behavior and their amplification from the observed system response to random variations. Spectral proper orthogonal decomposition (SPOD) is utilized to examine gains and phase relationships between signals from simulated data. Based on frequency domain and Fourier analysis, SPOD is employed to decompose forced oscillations into a set of space-time orthogonal modes. By such an approach, the ability of Fourier analysis to study stochastically forced oscillations in complex power system models is improved. Analytical criteria for describing the energy relationships in the observed oscillations are derived, and a physical interpretation of the stochastic modes is suggested. The methodology is demonstrated using simulated random oscillations of a realistic test power system.
Digital Image Protection Against Non-Authorized Artistic Edition

Nowadays, internet access and the sophisticated processing capacities of electronic devices for the capture and visualization of visual information, such as images; have allowed young users practices such as downloading, storing, editing, and re-sharing of images without authorization of the image owner. To solve this problem a watermarking algorithm for digital images is proposed, which is able to warranty the image protection against the most common editions incorporated into the capture devices and artistic editions integrated into the upload option of the social media. The experimental results show a high performance of the proposed algorithm, due to, a minimum edition of the protected image exhibiting to the naked eye the watermark pattern, by another way, the average performance is reported with a PSNR=42dB and NCD=0.0885.
Upaya Meningkatkan Hasil Belajar Siswa dengan Menerapkan Model Pembelajaran Example Non Example Tentang Hubungan Makanan dan Kesehatan pada Pelajaran IPA Di Kelas V SD Negeri 140 Bengkulu Utara

Factors that prompted this research included the teacher's lack of attention to students' thinking skills, the use of methods that were not varied, which resulted in learning patterns that tended to be rote, and the challenge of increasing students' desire to learn. How can applying an example non example model regarding the relationship between food and health improve the academic achievement of fifth grade students at SDN 140 North Bengkulu? Class V students were the subject of the study, which included 17 students, Five boys and Twelve girls, in the 2022/2023 academic year. The research process consists of planning, implementation, observation/observation, and evaluation-reflection. Students have greater mastery of learning materials, based on the results of improved learning. By using an example non-example learning model, learning outcomes regarding the relationship between food and health in science lessons in class V SD Negeri 140 North Bengkulu can be improved. This is evidenced by an increase in the average daily repetition 1 to 63.00 to 70.00, the average daily repetition 3 to75.33, and the average daily repetition 4 to 82.52.
Bridging the Data Divide: Secure Sharing and Governance for Effective Cross-Agency and Industry Collaboration

Collaboration between public and private sectors is essential in today's complicated environment to tackle issues like financial crime and public health crises. Interoperability problems and complex data governance requirements are obstacles to safe data exchange, an essential part of this kind of cooperation. This study delves into the topic of safe data sharing methods that allow for collaboration between commercial companies (such pharma and fintech) and federal, state, and municipal governments. Data standardization and access control are among of the issues discussed, with examples from the pharmaceutical and financial technology industries to back up the claims of effective solutions. To overcome the data gap and harness the potential of industry and cross-agency cooperation for mutual benefit, this study argues for strong data governance frameworks and interoperable technology
ELEMENTARY SCHOOL STUDENTS HAVE THE FORMATION OF READING SKILLS

The article will focus on the formation and development of reading skills in elementary school students. Definitions of the following terms in pedagogy are given: “reading skill", “reading technique", “reading comprehension", “correct reading", “fluent reading", “expressive reading", “intonation". 4 components of the reading skill are considered, as well as the conditions for managing the process of forming the reading skill. Along with the term "learning skill", the concept of "learning technique" is used. Study technique is a study method, pace and its correctness. Reading technique and comprehension are considered as two sides of the learning process. Technique is an integral part of reading comprehension. And the main purpose of reading is understanding. All of these learning components are interrelated and interdependent. Thus, the process of teaching a student to read should be built, taking into account the simultaneous work on all four components of reading skills, forming all these components in a complex and closely related relationship.
USING PEDAGOGICAL SITUATIONS AS A METHOD OF FORMING PROFESSIONAL AND SPECIAL COMPETENCIES

In this article, pedagogical situations are used as a way to form professional and special competencies. The decision of pedagogical situations by future teachers can be influenced by cognitive distortions that impede the development of objective, adequate pedagogical actions and, accordingly, inhibit pedagogical communication between the teacher and the future teachers. The solution to the pedagogical situation is carried out in individual and group formats. Depending on the nature of the future teachers' activities, we distinguish pedagogical situations of two levels: 1) technological - solving the situations in accordance with the methods of problem-based teaching; 2) improvisational - the solution of the pedagogical situation and its implementation coincide in time. This article substantiates the main stages of experimental research on testing the hypothesis and solving the set objectives, reveals the logics and describes the methodology for conducting experimental work, presents its results and formulates conclusions.
Bibliometric analysis on principles of digital content creation for toddlers

oung toddlers’ early experience of digital screen-watching engagement is extraordinary for their first three years of cognitive, language, physical, social, and emotional development. While previous studies have doc-umented that toddlers’ digital use negatively impacts from an ecological perspective. However, some work has shown that digital screen media opens learning outcomes for toddlers around three years old. Besides, there are significant literature gaps related to well-designed, theoretically proven, empirically researched principles of digital content creation for young toddlers. Therefore, the present study used inclusion and ex-clusion criteria, a PRISMA (The Preferred Reporting Items for Systematic reviews and Meta-Analyses) flow chart, and screening research hot spots to reveal the principles of creating digital content for toddlers aged 0 to 3. Bibliometric analysis on research hotspots and leading researchers’ publications evokes constitute the trends in digital content creation and consumption. The findings showed a compilation of eleven principles of digital content creation for young screen consumers based on the screening process from the Dimension data-base. This study used the bibliometric analysis method to seek publications on principles of digital content creation for young toddlers to provide further research directions in empirical research
Conditions of preschool teachers’ formation in higher education institutions

The purpose of the article is to providereaderswith information onthe actual problem of preschool teachers’ training —conditions of future preschool pedagogues’successful learning in higher education institutions.The present state oftheeducational environment plays an important role in the process of pedagogues’ for-mation.The article focuses on the theoretical methodologyof the problem, revealing several reviewsof peda-gogical and psychological research. The authors point out several reasons forstudents' successfulor unsuc-cessfulstudying process in higher educational institutions, such as motivation, self-esteem, type of thinking, communication level, level ofcreative thinking,and kinds of relationshipsamong students.Attention is drawn to analyzing these features, methods of evaluation, and results in calculations for them to improve the effectiveness of the educational process.A set of active and interactive methods and educational events for students aimed at improvingthe level of motivation, self-esteem, type of thinking, communication level, level of creative thinking,and kinds of relationships among students is proposed.An experiment was conducted and described in which a set of methods and educational eventswas tested. The results of the experiment showed a positive impact of the selected methods and activities in the preparation of preschool education spe-cialists.