Reviewerly/exHarmony huggingface dataset:
A Multi-Scale Cross-Fusion Medical Image Segmentation Network Based on Dual-Attention Mechanism Transformer

The U-net network, with its simple and powerful encoder–decoder structure, dominates the field of medical image segmentation. However, convolution operations are limited by receptive fields. They do not have the ability to model long-range dependencies, but Transformer has the capability of long-term modeling thanks to its core self-attention mechanism, which has been widely applied in the field of medical image segmentation. However, both CNNs and Transformer can only perform correlation calculations for a single sample, ignoring the correlation between different samples. To address these problems, we propose a new Transformer, which we call the Dual-Attention Transformer (DAT). This module captures correlations within a single sample while also learning correlations between different samples. The current U-net and some of its variant models have the problem of inadequate feature fusion, so we also improve the skip connection to strengthen the association between feature maps at different scales, reduce the semantic gap between the encoder and decoder, and further improve the segmentation performance. We refer to this structure as DATUnet. We conducted extensive experiments on the Synapse and ACDC datasets to validate the superior performance of our network, and we achieved an average DSC (%) of 83.6 and 90.9 and an average HD95 of 13.99 and 1.466 for the Synapse and ACDC datasets, respectively.
Application of Knowledge Graph Technology with Integrated Feature Data in Spacecraft Anomaly Detection

Given the complexity of spacecraft system structures and functions, existing data-driven methods for anomaly detection face issues of insufficient interpretability and excessive dependence on historical data. To address these challenging problems, this paper proposes a method for applying knowledge graph technology with integrated feature data in spacecraft anomaly detection. First, the ontology concepts of the spacecraft equipment knowledge graph are designed according to expert knowledge, and then feature data are extracted from the historical operation data of the spacecraft in various states to build a rich spacecraft equipment knowledge graph. Next, spacecraft anomaly event knowledge graphs are constructed based on various types of anomaly features. During spacecraft operation, telemetry data are matched with the feature data in the knowledge graph, enabling anomaly device location and anomaly cause judgment. Experimental results show that this method, which utilizes spacecraft anomaly prior knowledge for anomaly detection and causes interpretation, has high practicality and efficiency. This research demonstrates the promising application prospects of knowledge graph technology in the field of spacecraft anomaly detection.
Practical Real-Time Phase Drift Compensation Scheme for Quantum Communication Systems

Quantum communication systems are susceptible to various perturbations and drifts arising from the operational environment, with phase drift being a crucial challenge. In this paper, we propose an efficient real-time phase drift compensation scheme in which only existing data from the quantum communication process is used to establish a stable closed-loop control subsystem for phase tracking. This scheme ensures the continuous operation of transmission by tracking and compensating for phase drift in the phase-encoding quantum communication system. The experimental results demonstrate the effectiveness and feasibility of the proposed scheme with an average quantum bit error rate of 1.60% and a standard deviation of 0.0583% for 16 h of continuous operation.
TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.
Urban Resilience of Important Node Cities in Population Migration under the Influence of COVID-19 Based on Mamdani Fuzzy Inference System

COVID-19 has resulted in a great inconvenience and has had a severe impact on the economy and residents’ daily life in China and even the world. Urban resilience, as the key representation of social and economic stability, can directly reflect the development and stability of cities. In addition, the Mamdani fuzzy inference system (MFIS), as one of the commonly used fuzzy inference systems, has been successfully applied in various application problems involving imprecise or vague information since it was proposed. In this paper, we mainly consider the urban resilience of 50 important node cities for population migration (50INCPM) in China in 2020 under the influence of COVID-19. We apply MFIS for approximating the urban resilience index (URI) based on multiple inputs, which includes the population density resilience index (PRI), gross domestic product per capita resilience index (GRI), in-degree centrality resilience index (IRI), out-degree centrality resilience index (ORI), confirmed cases number (CCN), recovery rate (RR) and mortality rate (MR). Meanwhile, based on the big data of population migration and COVID-19 data in China from 15 January to 15 March in 2020, we calculate the URI of 50INCPM in China in 2020 under the influence of COVID-19. Moreover, we show the spatial difference of URI and its changes in different stages. The results show that (1) the URI of 50INCPM decreases from the eastern coastal area to the western inland, and the cities with URI more than 0.5 are gathered in the eastern coastal area of China. As COVID-19 is controlled, the URI is gradually rising, and the growth rate of URI in southeast coastal cities exceeds that of inland cities. (2) The second-tier and third-tier cities have stronger resilience in the case of large-scale emergencies. (3) There exists a positive correlation in URI and RR. The expectation of the research finding gives a basis for judging the economic and social situation under the impact of COVID-19, which can help local governments accurately judge city resilience, and provide a reference for the decision on resuming production and work, so it is of positive significance for national economic resilience and social stability. Finally, on the basis of universal vaccine coverage, we hold that the GOC should promote the cities’ resilience in China, especially in the first-tier city in inland China (Beijing, Shanghai, Guangzhou and Shenzhen). On the other hand, on the premise of implementing epidemic prevention and control measures, local governments should stimulate the resilience of each city in terms of population and economy.
Fuzzy Transform Image Compression in the YUV Space

This research proposes a new image compression method based on the F1-transform which improves the quality of the reconstructed image without increasing the coding/decoding CPU time. The advantage of compressing color images in the YUV space is due to the fact that while the three bands Red, Green and Blue are equally perceived by the human eye, in YUV space most of the image information perceived by the human eye is contained in the Y band, as opposed to the U and V bands. Using this advantage, we construct a new color image compression algorithm based on F1-transform in which the image compression is accomplished in the YUV space, so that better-quality compressed images can be obtained without increasing the execution time. The results of tests performed on a set of color images show that our color image compression method improves the quality of the decoded images with respect to the image compression algorithms JPEG, F1-transform on the RGB color space and F-transform on the YUV color space, regardless of the selected compression rate and with comparable CPU times.
Digital Communication Forensics in 6G and beyond Networks

6G networks can be truly intelligent wireless systems that have the flexibility for continuous technological improvements. However, security events affecting information systems have grown increasingly complicated and sophisticated. Intruders may be able to operate due to a lack of evidence authenticity. Using network forensics methods can solve many cybercrime instances. These methods can be used to locate the intruder and to determine the intrusion’s origin and how it can be avoided in the future. Existing network forensics techniques confront security threats and vulnerabilities in several networks, such as personal area networks, local area networks, wide area networks, and wireless wide area networks under telecom networks, in practice, but cannot operate in 6G and beyond telecom networks, which are critical at this moment. Thus, a comprehensive literature analysis of telecom networks is conducted to identify existing concerns and challenges in conducting digital communication forensics. Hence, a digital forensics system for 6G and beyond networks is proposed as an initial idea on the basis of the highlighted issues, thereby assessing security incidents and providing justified evidence that explains the footprints of the attackers.
Two Residual Attention Convolution Models to Recover Underexposed and Overexposed Images

Inconsistent lighting phenomena in digital images, such as underexposure and overexposure, pose challenges in computer vision. Many studies have developed to address these issues. However, most of these techniques cannot remedy both exposure problems simultaneously. Meanwhile, existing methods that claim to be capable of handling these cases have not yielded optimal results, especially for images with blur and noise distortions. Therefore, this study proposes a system to improve underexposed and overexposed photos, consisting of two different residual attention convolution networks with the CIELab color space as the input. The first model working on the L-channel (luminance) is responsible for recovering degraded image illumination by using residual memory block networks with self-attention layers. The next model based on dense residual attention networks aims to restore degraded image colors using ab-channels (chromatic). A properly exposed image is produced by fusing the output of these models and converting them to RGB color space. Experiments on degraded synthetic images from two public datasets and one real-life exposure dataset demonstrate that the proposed system outperforms the state-of-the-art algorithms in optimal illumination and color correction outcomes for underexposed and overexposed images.
PENGARUH ADIKSI GAME ONLINE MOBILE LEGEND TERHADAP KECERDASAN EMOSIONAL SISWA

This research was conducted with the aim of examining the Effect of Mobile Legend Online Game Addiction on the Emotional Intelligence of Students of SMAN 44 Jakarta. The Mobile Legend Online Game was chosen because it is considered to have a significant influence on students' emotional intelligence in the ever-developing digital technology era. The population of this study were students of class XI at SMAN 44 Jakarta as subjects in this study, with a total of 68 students divided into 6 classes (3 classes XI IPS and 3 classes XI MIPA), each class consisting of 36 students. The research sample was taken using purposive sampling technique and using observation and questionnaires as data collection tools. The results showed that there was a significant influence between online legend mobile game addiction and students' emotional intelligence. This can be seen from the results of statistical calculations, where the Fcount value of 15,328 exceeds the Ftable of 3.98 with a significance level of 0.001 &lt;0.05. based on the established criteria, Ha's hypothesis which states addiction to online mobile legend games has an influence on emotional intelligence is accepted. The mobile legend online game addiction variable has an influence of 1.88%, while other factors not included in the research model can also affect emotional intelligence.
Spot Invalid Point Repair Algorithm of Detector Array Measurement System Based on Image Correlation Coefficient

The detector array method has been widely used in the field of high-energy laser far-field spot parameter measurement due to its ability to directly measure the far-field spot of high-energy lasers, wide dynamic range of the detectors, high system sampling frequency, good real-time performance, and suitability for various testing environment requirements. However, during the measurement process, the irradiation of strong lasers or damage to other hardware systems can result in invalid points in the acquired spot images, thereby reducing the measurement accuracy of the system. In order to achieve accurate measurement of laser far-field spot parameters, this paper establishes an experimental model based on the analysis of the sampling spacing of the detector array target and proposes a laser far-field spot invalid point repair algorithm based on image correlation coefficient. Experimental results demonstrate that the algorithm proposed in this paper effectively reduces the impact of invalid points in the measurement system on the measurement accuracy, and achieves accurate measurement of high-energy laser measurement systems.
Sok: vector OLE-based zero-knowledge protocols

Abstract A zero-knowledge proof is a cryptographic protocol where a prover can convince a verifier that a statement is true, without revealing any further information except for the truth of the statement. This article is a survey of recent developments in building practical zero-knowledge proof systems using vector oblivious linear evaluation (VOLE), a tool from secure two-party computation. In this work, we attempt to systematize the recent works on VOLE-based Zero-Knowledge proofs and make the state of the art accessible in one document.
PyDGN: a Python Library for Flexible and Reproducible Research on Deep Learning for Graphs

The use of standardized evaluation procedures is a key component in the Machine Learning (ML) field to determine whether new approaches grant real advantages over others.This is especially true for fast-growing research areas, where a substantial amount of literature relentlessly appears every day.In the graph machine learning field, some evaluation issues have already been brought to light and partially addressed, but a general-purpose library for rigorous evaluations and reproducible experiments is lacking.We therefore introduce a new Python library, called PyDGN, to provide users with a system that lets them focus on models' development while ensuring empirical rigor and reproducibility of their results.
Hand Gesture Recognition System based on 60 GHz FMCW Radar and Deep Neural Network

The proposed study provides a novel technique for recognizing hand gestures that use a combination of Deep Convolutional Neural Networks (DCNN) and 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The motion of a Human's hand is detected using the FMCW radar, and the various gestures are classified using the DCNN. Motion detection and frequency analysis are two techniques that the suggested system combines. The basis of the capability of motion detection in FMCW radars' is to recognize the Doppler shift in the received signal brought on by the target's motion. To properly identify the hand motions, the presented technique combines these two techniques. The system is analyzed using a collection of hand gesture photos, and the outcomes are analyzed with those of other hand gesture recognition systems which are already in use. A dataset of five different hand gestures is used to examine the proposed system. According to the experimental data, the suggested system can recognize gestures with an accuracy of 96.5%, showing its potential as a productive gesture recognition system. Additionally, the suggested system has a processing time of 100 ms and can run in real time. The outcomes also demonstrate the proposed system's resistance to noise and its ability to recognize gestures in a variety of configurations. For gesture detection applications in virtual reality and augmented reality systems, this research offers a promising approach.
Robust medical image watermarking in frequency domain

Protecting patient information in medical image watermarking poses a significant challenge, especially when traditional methods like the Arnold transform prove inadequate in ensuring security. This paper introduces a novel approach within the Discrete Wavelet Transform (DWT) domain to address this issue effectively. By employing the Advanced Encryption Standard (AES), the security and robustness of the system are greatly enhanced through the encryption of both the medical image and patient data. The encrypted medical image undergoes a 2-level DWT process, allowing the concealment of encrypted patient information while maintaining its invisibility. This proposed scheme surpasses others in experimental evaluations, as evidenced by metrics such as PSNR and NC, solidifying its position as a more secure choice for medical image watermarking. The results validate the scheme’s robustness and imperceptibility.
Enhancing Income as a Starting Point for Designing Sustainable Health Product-Service Systems for Chronically Ill Elderly in Rural Areas

Insufficient income, inadequate healthcare resources, and insufficient chronic disease management awareness pose a fundamental obstacle to the sustained health maintenance of elderly individuals with chronic diseases in rural areas. In response to these challenges, this study adopted a research-through-design approach and introduced system design thinking by incorporating new stakeholders, experiential insights, and services. Initially, during the background research stage, a semi-structured interview was conducted to gain an understanding of the pains, needs, and potential opportunities of the target users. Subsequently, in the design model construction stage, desk research was carried out to refine and integrate the common components identified in various design models. In a focus group, these components were then incorporated into the development of a Sustainable Health Product-Service System Design Model tailored for rural elderly with chronic diseases after refining the pains, needs, and potential opportunities defined in the first stage. This design model encompassed elements including a background layer, a stakeholder layer, a funding layer, a product layer, a service layer, and an information layer. In the system design and design model validation stage, a design project aimed at health maintenance was launched within Liyang Town, with the design thinking method following a process of empathizing, defining, ideating, prototyping, and testing. Throughout the project, the established model guided the design process and facilitated the conceptualization and implementation of a Product-Service system. In conclusion, an investigation encompassing 15 elderly individuals was undertaken to analyze their income balance, health condition, health awareness, and their satisfaction with the health system. This analysis spanned the period both before and after their involvement in the project. The outcomes of this examination revealed notable enhancements across all three dimensions evaluated, highlighting the positive impact of the project on the elderly participants. Furthermore, the project’s social benefits were also comprehensively established.
Elevation Based Outdoor Navigation with Coordinated Heterogeneous Robot Team

Today, the use of heterogeneous robot teams is increasing in military operations and monitoring the environment. However, single 2D autonomous navigation systems fail in rough terrains. In this study, a solution to this problem is proposed using 2.5D navigation that takes into account the slopes on the elevation map. Using ROS and Gazebo, we coordinate drones and ground vehicles to process terrain elevations. The simulation world used in the study reflects a real-world rough terrain and also some urban artifacts are added to the simulation world. Husky simulation model is used as the ground vehicle utilizing 3D LIDAR, GPS and 9 DOF IMU sensors, which can output 3D map and 3D localization using 3D SLAM. Using the SLAM localization, a 2.5D map is created on the ground vehicle. Drone simulation model, similarly equipped, follows the ground vehicle with a GPS-based waypoint navigation and can create a 2.5D map using its sensors. A global plan is created for the ground vehicle by cooperative effort of both robots, using the map information from ground vehicle where available and using the map information from the drone where ground vehicle’s map is insufficient. 2.5D navigation of the ground vehicle is carried out by the local planner taking into account the calculated cooperative global path. Proposed method results in shorter routes and fewer path planning issues. This is shown by the comparative analysis where the ground vehicle or the drone is used alone.
Digital Transformation with Artificial Intelligence in the Insurance Industry

In today's world artificial intelligence studies are applied in many sectors. There are companies in the insurance field that use data-driven and innovative technologies. The insurance industry depends on the ability to be competitive in the market with the right analysis of customer data and the right products. The insurance industry is a challenging area where customer data needs to be analyzed correctly. Artificial intelligence-supported analysis of customer behavior provides a competitive and more profitable structure. All steps from actuarial calculation to customer claim transactions can become much more effective with artificial intelligence. In the research, artificial intelligence products used by today's insurance companies were examined. An alternative model based on real practice data, much more detailed than other studies in the literature, is recommended in the research. The research offers innovative approaches to artificial intelligence and digital transformation studies.