Reviewerly/exHarmony huggingface dataset:
Multi-Task Learning for Lung Disease Classification and Report Generation via Prior Graph Structure and Contrastive Learning

Analyzing medical records takes a large amount of time and resources for medical workers. Therefore, the advancement of report generation technology is critical for saving doctors’ time while producing reports. Some diseases frequently exhibit co-occurrence interactions in the context of medical picture categorization and report creation, which necessitate hu-man-designed and added representation. To further capture this latent information, we propose creating a disease co-occurrence matrix from the existing dataset and training a graph neural network on it. We use multi-label contrastive learning to assist the model in distinguishing the mutual links between different diseases. Meanwhile, we incorporate report retrieval module and decoder model to complete the report generation task, and the multi-task learning of disease classification and report generation can improve the generalization ability of the target task. Experimental results show that our pro-posed combined method and multi-task learning approach have shown significant improvement compared to previous research.
RF-CGR: Enable Chinese Character Gesture Recognition With RFID

Gesture recognition is the basis of human-computer interaction (HCI), and RFID has attracted wide attention for its advantages of lightweight, low cost, and universality. We propose RF-CGR, which implements cross-domain Chinese character gesture recognition intuitively and effectively. The fundamental concept of RF-CGR is to extract hand motion patterns from RFID phase information and display distinctive gesture features. Firstly, we build a signal sensing model to capture gesture information effectively. Secondly, we innovatively convert the Chinese character phase signal into an intuitive picture representation and use the gesture movement change pattern as input through data pre-processing operations. Finally, Modify-Visual Geometry Group Network (M-VGG) model is used for cross-domain gesture recognition. The M-VGG network learns high-level semantic features of gestures through spatiotemporal convolutions to extract cross-domain gesture features. Compared with other models, M-VGG has fewer parameters, and shorter training time, while still achieving high-accuracy gesture recognition. We evaluate its performance in three scenarios. The accuracy is 98.75% and 98% for new users and new scenarios respectively, significantly outperforming existing wireless signal-based gesture recognition methods.
DNA-Depth: A Frequency-Based Day-Night Adaptation for Monocular Depth Estimation

Autonomous driving necessitates ensuring safety across diverse environments, particularly in challenging conditions like low-light or nighttime scenarios. As a fundamental task in autonomous driving, monocular depth estimation has garnered significant attention and discussion. However, current monocular depth estimation methods primarily rely on daytime images, which limits their applicability to nighttime scenarios due to the substantial domain shift between daytime and nighttime styles. In this paper, we propose a novel Day-Night Adaptation method (DNA-Depth) to realize monocular depth estimation in a night environment. Specifically, we simply use Fourier Transform to address the domain alignment problem. Our method does not require extra adversarial optimization but is quite effective. The simplicity of our method makes it easy to guide day-to-night domains. To the best of our knowledge, we are the first to utilize fast Fourier transformation for nighttime monocular depth estimation. Furthermore, to alleviate the problem of mobile light sources, we utilize an unsupervised joint learning framework for depth, optical flow, and ego-motion estimation in an end-to-end manner, which are coupled by 3D geometry cues. Our model can simultaneously reason about the camera motion, the depth of a static background, and the optical flow of moving objects. Extensive experiments on the Oxford RobotCar, nuScenes, and Synthia datasets demonstrate the accuracy and precision of our method by comparing it with those state-of-the-art algorithms in depth estimation, both qualitatively and quantitatively.
Target Tracking of UUV Based on Maximum Correntropy High-Order UGHF

Underwater target tracking with signal propagation delay is a major challenge. The widely-used unscented Gauss-Helmert filter (UGHF) algorithm shows good tracking performance in bearing-only filters with signal propagation delay. However, UGHF algorithm is a nonlinear filtering method based on 2-order unscented transformation (UT), which can only match the accuracy of the 3-order Taylor expansion term of Gauss-Hermite model (GHM). Moreover, UGHF is a Gaussian filter, and when the measurement noise presents non-Gaussian characteristics, the tracking accuracy will be reduced. In order to improve the tracking accuracy of underwater bearing-only target, this paper proposes a high-order UGHF (HOUGHF) algorithm using high-order UT instead of 2-order UT. In order to further improve the tracking accuracy of the tracking algorithm under non-Gaussian noise, in this paper, maximum correntropy criterion (MCC) is used to replace the original minimum mean square error (MMSE) to update HOGGHF’s posterior estimation. The maximum correntropy HOGGHF (MCHOGGHF) algorithm is proposed by using MCC based on Gaussian kernel function. At the same time, in order to deal with the change of measurement noise, a method of adaptive Gaussian kernel width is presented. Simulation and sea trial results show that the two algorithm are superior to UGHF algorithm and MCHOGGH algorithm is superior to HOGGHF algorithm in estimation accuracy under non-Gaussian noise.
MCWESRGAN: Improving Enhanced Super-Resolution Generative Adversarial Network for Satellite Images

With the dynamic technological development, we are witnessing a major progress in solutions that allow for the observation of Earth's surface. The constructed satellites are becoming still smaller and lighter. Small satellites (those weighing less than 100kg) have a significant drawback (in terms of observational capabilities). Due to their limitations, the installed optic systems are not perfect. As a result, the quality of the obtained images is lower, including lower resolution, although the satellites move on the Low Earth Orbit. In the case of images lacking a high-resolution counterpart, the spatial resolution of the imagery can be improved using single-image super-resolution (SISR) algorithms. In this article, we present an SISR solution based on a new network called MCWESRGAN, which is a modification of the popular ESRGAN network. We propose a novel strategy that introduces a multi-column discriminator model. The generator model is trained using Wasserstein loss. The introduced modifications enable a tenfold reduction in the training time of the network. The proposed algorithm is verified using images obtained from space, aerial imagery, and the Dataset for Object deTection in Aerial Images (DOTA) database. A set of evaluation methods for super-resolution (SR) images is proposed to verify the results. These evaluation methods indicate areas that are poorly estimated by the algorithm. Furthermore, as part of the conducted experiments, an absolute assessment method for interpretational potential based on the power spectral density of the image (PSD) is proposed, allowing for determining the magnitude of interpretational improvement after applying resolution enhancement algorithms. The conducted research demonstrates that we achieve better qualitative and quantitative results than classical ESRGAN methods and other state-of-the-art (SOTA) approaches.
GAN-IoTVS: A Novel Internet of Multimedia Things-Enabled Video Streaming Compression Model Using GAN and Fuzzy Logic

This study proposes a novel method for streaming data compression and encoding protocols based on generative adversarial networks (GANs) and fuzzy logic. The concept of GAN and fuzzy logic integration creates a different prospect as compared to the existing multimedia streaming data compression and security protocols. This article proposed a collaborative model for multimedia data compression by integrating GAN with fuzzy logic. It guarantees data compression using a secure channel for the verification platform of multimedia-enabled videos. Thus, it defines a fuzzy-based weight function for controlling the frequency of the current objects by the contents that effectively handle a string of code generation, which is designed and developed. It also eliminates adversarial loss removes streaming data redundancy and saves resource constraints using static–dynamic memory, transmission bandwidth, and computational power. In addition, this proposed model reduces the number of lossless video files before encryption by increasing the entropy of the sequence of coded images. Fuzzy logic code provides a more secure multimedia information mapping on the code contents. Substantially, the private key is generated when multimedia-enabled video files are embedded and sent to the receiver, and decoding information is possible using the same key. To illustrate the simulation of the theoretical results and model experiments are performed under the Python 3.9 tool. The experimental results show that a better compression ratio is achieved compared with the other state-of-the-art models. There is no difference between the original video and the compression after decoding, and the rate is increased to over 30.13%.
Revisiting the Effectiveness of 3D Object Recognition Benchmarks

Recently, 3D computer vision has greatly emerged and become essential topic in both research and industry applications. Yet large scale 3D benchmark like ImageNet is not available for many 3D computer vision tasks such as 3D object recognition, 3D body motion recognition, and 3D scene understanding. Existing 3D benchmarks are not enough in the number of classes and quality of data samples, and reported performances on the datasets are nearly saturated. Furthermore, 3D data obtained with existing 3D sensors are noisy and incomplete causing unreliable evaluation results. In this work, we revisit the effectiveness of existing 3D computer vision benchmarks. We propose to refine and re-organize existing benchmarks to provide cheap and easy access but challenging, effective and reliable evaluation schemes. Our task includes data refinement, class category adjusting, and improved evaluation protocols. Biased benchmark subsets and new challenges are suggested. Our experimental evaluations on ModelNet40, a 3D object recognition benchmark, show that our revised benchmark datasets (MN40-CR and MN20-CB) provide improved indicators for performance comparison and reveals new aspects of existing methods. State-of-the-art 3D object classification and data augmentation methods are evaluated on MN40-CR and MN20-CB. Based on our extensive evaluation, we conclude that existing benchmarks that are carefully re-organized are good alternatives of large scale benchmark which is very expensive to build and difficult to guarantee data quality under immature 3D data acquisition environment. We make our new benchmarks and evaluations public.
Disk Forensics of VxWorks File Systems for Aircraft Security Analyse

Modern avionics systems exhibit numerous networked electronic components ranging from sensors and actuators to dedicated subsystems, resulting in aircraft capable of processing and responding to information accurately, reliably, and in a timely fashion. Assuring the cyber security of these systems is a continual challenge and an active area of research; in the case where an aircraft has been compromised by a malicious actor, digital forensics can be utilized to investigate what and how the incident occurred. This research answers a simple, yet fundamental question on the security of aircraft: whether useful digital forensic artifacts be obtained from embedded real-time systems on aircraft. The highly reliable file system (HRFS) utilized by VxWorks was analyzed and described to align with the generalized descriptions of file system formats accepted in academia. The Sleuth Kit (TSK), an open-source forensic toolkit, was analyzed and extended to include functionality to support this file system, and a proof-of-concept implementation to obtain digital forensic artifacts from real-time operating systems on aircraft was developed. This research finds that the proposed implementation can perform file analysis and recovery from a VxWorks generated HRFS-formatted file system and can be generalized to show that embedded real-time systems can provide useful digital forensic artifacts.
The first decade of he Public Library of Uzice on Facebook

The appearance of social networks has brought the opportunity to cultural institutions to present themselves in a new and modern way, which primarily involves simple and fast communication with users in the virtual world. This paper presents review of the last decade, how long the Public Library of Uzice is present on Facebook. It is described in which ways, through this social network, programs, services and novelties in the work of the Library were promoted. The categorization of news being published on the page is made in accordance with the type of content that is promoted. One part of the paper contains statistical data on the number of people who follow the page, their gender and age structure, and a comparison between virtual and physical visitors of the Library. A special part is dedicated to the contents that were published during the pandemic caused by the corona virus. The pandemic and the measures that followed it reversed everything that libraries are and have been until then. Users were denied the right to borrow books, and librarians faced a new challenge - what to offer readers without options of physical engagement.
Revision and write-off of the monographic publications of the Public Library of Užice

The process of the revision in libraries is defined by the Law on Library and Information Activities and the Rulebook on Inventory, Processing, Revision and Write-Off of Library and Information Materials and Sources. The revision of the fund in libraries on the one hand is a legal obligation and on the other hand gives a precise insight into the state of the fund, contributes to its relevance, opens such a valuable space for new publications. The paper presents the experiences gained during the work on the automated revision of the holdings of the Public Library of Užice in the new Cobiss environment. They can be use by libraries that will revision their funds in this way in the coming period. The aim of this paper is to offer ways to resolve dilemmas and doubts that may arise in certain phases of the automated revision process. Thanks to the benefits of the new Cobiss / Inventory environment, this revision is the most thorough to date and most accurately reflects the state of the fund.
A qualitative study of penetration testers and what they can tell us about information security in organisations

Purpose This paper presents a qualitative study of penetration testing, the practice of attacking information systems to find security vulnerabilities and fixing them. The purpose of this paper is to understand whether and to what extent penetration testing can reveal various socio-organisational factors of information security in organisations. In doing so, the paper innovates theory by using Routine Activity Theory together with phenomenology of information systems concepts. Design/methodology/approach The articulation of Routine Activity Theory and phenomenology emerged inductively from the data analysis. The data consists of 24 qualitative interviews conducted with penetration testers, analysed with thematic analysis. Findings The starting assumption is that penetration testers are akin to offenders in a crime situation, dealing with targets and the absence of capable guardians. A key finding is that penetration testers described their targets as an installed base, highlighting how vulnerabilities, which make a target suitable, often emerge from properties of the existing built digital environments. This includes systems that are forgotten or lack ongoing maintenance. Moreover, penetration testers highlighted that although the testing is often predicated on planned methodologies, often they resort to serendipitous practices such as improvisation. Originality/value This paper contributes to theory, showing how Routine Activity Theory and phenomenological concepts can work together in the study of socio-organisational factors of information security. This contribution stems from considering that much research on information security focuses on the internal actions of organisations. The study of penetration testing as a proxy of real attacks allows novel insights into socio-organisational factors of information security in organisations.
Sustainable Multi-Author Writing Style Analysis for Identifying Stylistic Differences Between Authors

Natural language processing (NLP) is a sustainable subfield of Artificial Intelligence that focuses on the interaction between computers and humans through natural language. NLP algorithms enable computers to comprehensively understand, interpret, and generate human language, thus facilitating the sustainable analysis and comprehension of vast amounts of textual data. Within the context of sustainable style change detection, NLP algorithms play a pivotal role in analyzing multi-author documents and identifying the points at which authors transition. This sustainable step is critical in authorship recognition as it furnishes a more precise comprehension of which sections were authored by different individuals. A multi-author document’s writing style can evolve over time, and this sustainability can prove invaluable in fields such as forensics, journalism, and literary studies, among others.The sustainable goal of this project is to investigate various NLP methods for sustainable style change detection. By scrutinizing datasets and juxtaposing them with advanced methodologies in the existing literature, the effectiveness of these strategies will be ascertained. The overarching aim of our study is to foster the progress of both the field of NLP research and its sustainable practical applications.
End-to-end wavelet block feature purification network for efficient and effective UAV object tracking

Recently, unmanned aerial vehicle (UAV) object tracking tasks have significantly improved with the emergence of deep learning. However, owing to the object feature pollution caused by motion blur, illumination variation, and occlusion, most of the existing trackers often fail to precisely localize the target in the complex real-world circumstances. To overcome this challenge, we present a novel wavelet block feature purification network (WFPN) for efficient and effective UAV tracking. WFPN is mainly composed of downsampling network through wavelet transforms and upsampling network through inverse wavelet transforms. To be specific, the downsampling network performs discrete wavelet transform (DWT) to reduce interference information and preserve original feature details, while the upsampling network applies inverse DWT (IDWT) to reconstruct decontaminated feature information. Additionally, a novel sequential encoder is introduced to achieve a better purification effect. Finally, a pooling distance loss is devised to improve the purification effect of DWT downsampling network. Extensive experiments show that our WFPN achieves promising tracking performance on three well-known UAV benchmarks, especially on sequences with feature pollution. Moreover, our method runs at 33.2 frames per second on the edge platform of Nvidia Jetson AGX Orin, which is suitable for UAVs with limited onboard payload and computing capability.
INNOVATIVE METHODS IN ENVIRONMENTAL EDUCATION OF HIGHER EDUCATION INSTITUTIONS: PRACTICAL ORIENTATION AND METHODOLOGY OF USE

Innovative methods of teaching ecology can be useful for students and teachers, as they provide more effective learning and understanding of the material. The advantages of innovative methods include the effective use of modern technologies in teaching, providing a practical component and the formation of critical and creative thinking in students. However, the introduction of innovative methods of teaching ecology can be fraught with challenges. One of the main challenges is that teachers need to have a sufficient level of competence in using modern technologies and developing effective curricula.&#x0D; This article provides readers with useful information on the use of innovative methods of teaching ecology. It will allow you to get acquainted with various practical methods and their advantages, which will contribute to more effective learning of the material by students and increase their level of training in the field of ecology. In addition, the article may be useful for teachers who are looking for new approaches to teaching ecology and want to improve the quality of their disciplines.
DECORATIVE AND APPLIED CREATIVITY AS A FACTOR FOR THE FORMATION OF THE CREATIVE PERSONALITY OF THE FUTURE TECHNOLOGY TEACHER

In modern conditions, education is an important factor in reviving the nation, instilling in young people a national consciousness, a harmoniously developed personality of a future teacher of technology – a bearer of the best assets of national and world culture, capable of self-development and self-improvement, to transfer experience to his schoolchildren.&#x0D; The article examines the peculiarities of decorative and applied creativity as a factor in the formation of the creative personality of a student of higher education. The effective role of folk art as a source of spiritual and aesthetic culture of the future technology teacher is revealed. The potential of decorative and applied creativity in the formation of the creative personality of the future technology teacher was revealed.
Extracting Domain-Specific Chinese Named Entities for Aviation Safety Reports: A Case Study

Aviation safety reports can provide detailed records of past aviation safety accidents, analyze their problems and hidden dangers, and help airlines and other aviation enterprises avoid similar accidents from happening again. In a novel way, we plan to use named entity recognition technology to quickly mine important information in reports, helping safety personnel improve efficiency. The development of intelligent civil aviation creates demands for the incorporation of big data and artificial intelligence. Because of the aviation-specific terms and the complexity of identifying named entity boundaries, the mining of aviation safety report texts is a challenging domain. This paper proposes a novel method for aviation safety report entity extraction. First, ten kinds of entities and sequences, such as event, company, city, operation, date, aircraft type, personnel, flight number, aircraft registration and aircraft part, were annotated using the BIO format. Second, we present a semantic representation enhancement approach through the fusion of enhanced representation through knowledge integration embedding (ERNIE), pinyin embedding and glyph embedding. Then, in order to improve the accuracy of specific entity extraction, we constructed and utilized the aviation domain dictionary which includes high-frequency technical aviation terms. After that, we adopted bilinear attention networks (BANs), the feature fusion approach originally used in multi-modal analysis, in our study to incorporate features extracted from both iterated dilated convolutional neural network (IDCNN) and bi-directional long short-term memory (BiLSTM) architectures. A case study of specific entity extraction for an aviation safety events dataset was conducted. The experimental results demonstrate that our proposed algorithm, with an F1 score reaching 97.93%, is superior to several baseline and advanced algorithms. Therefore, the proposed approach offers a robust methodological foundation for the relationship extraction and knowledge graph construction of aviation safety reports.