Reviewerly/exHarmony huggingface dataset:
Cyber-Physical Intrusion Detection System for Unmanned Aerial Vehicles

The increasing reliance on unmanned aerial vehicles (UAVs) has escalated the associated cyber risks. While machine learning has enabled intrusion detection systems (IDSs), current IDSs do not incorporate cyber-physical UAV features, which limits their detection performance. Additionally, the lack of public UAV’s cyber and physical datasets to develop IDS hinders further research. Therefore, this paper proposes a novel IDS fusing UAV cyber and physical features to improve detection capabilities. First, we developed a testbed that includes UAV, controller, and data collection tools to execute cyber-attacks and gather cyber and physical data under normal and attack conditions. We made this dataset publicly available. The dataset covers a range of cyber-attacks including denial-of-service, replay, evil twin, and false data injection attacks. Then, machine learning-based IDSs fusing cyber and physical features were trained to detect cyber-attacks using support vector machines, feedforward neural networks, recurrent neural networks with long short-term memory cells, and convolutional neural networks. Extensive experiments were conducted on varying complexity and range of attack training data to explore whether (a) fusion of cyber and physical features enhances detection performance compared to cyber or physical features alone, (b) fusion enhances detection when IDS is trained on a single attack type and tested on unseen attacks of varying complexity, (c) fusion enhances performance when the range of attack training data increases and models are tested on unseen attacks. Answering these research questions provides insights into IDS capabilities using cyber, physical, and cyber-physical features under different conditions.
Image Style Transfer Based on Deep Feature Rotation and Adaptive Instance Normalization

Style transfer is an attractive point in recent years. This tech needs an image to provide its style, and a neural network is applied to transfer the style onto a content target. Most of the existing methods aim to obtain the weight parameters by training on a single image feature and continuously optimizing the network structure to improve the computational efficiency and image quality, but these methods only consider the image features from a certain perspective, which may lead to some information loss. In this paper, we add Deep Feature Rotation (DFR) to the AdaIN network, which enables us to generate multiple features from one image feature by rotation and train these features synthetically. By this method, we can perform comprehensive feature extraction on a stylized image to preserve more complete feature information. We have tried different combinations of angles and also compared them with other methods. The code is available at https://github.com/SP-FA/Style-Transfer-Based-on-DFR-and-AdaIN
Performance Evaluation of TLS Session Establishments on an ARM Cortex-M4 Platform

The Transport Layer Security protocol is a widespread cryptographic protocol designed to provide secure communication over insecure networks by providing authenticity, integrity, and confidentiality. As a first step, in the TLS Handshake Protocol a common master secret is negotiated. In many configurations, this step makes considerable use of asymmetric cryptographic algorithms. It seems to be a prevalent assumption that the use of such asymmetric cryptographic algorithms is unsuitable for resource-constrained devices. Therefore, the work at hand analyzes the runtime performance of the TLS vl.2 session establishments on an embedded ARM Cortex-M4 platform. We measure the execution time to generate and parse session establishment messages for the client and server sides. In particular, we study the impact of different elliptic curves used for the ephemeral Diffie-Hellman key exchange and the impact of different lengths and subject public key algorithms of certification paths. Our analysis shows that the use of asymmetric cryptographic algorithms is well possible on resource-constrained devices, if carefully chosen and well implemented. This allows the use of the well-proven TLS protocol also for applications from the (Industrial) Internet of Things, including Fieldbus communication.
A grayscale mapping method for infrared images based on generative adversarial networks

The grayscale mapping of infrared images is an important research direction in the field of infrared image visualization. The grayscale mapping method of infrared images directly determines important visualization indicators such as detail preservation and overall perception of the original infrared images and can be considered as the foundation and guarantee for detail enhancement. Although the current mainstream grayscale mapping methods for infrared images can achieve good mapping results, there is still room for improvement in terms of preserving image details and enhancing image contrast. In this paper, we propose a grayscale mapping method for infrared images based on generative adversarial networks. Firstly, our discriminator adopts a unique global-local structure, which allows the network to consider both global and local losses when calculating the loss, effectively improving the image quality in local regions of the mapped image. Secondly, we introduce perceptual loss in the loss function, which ensures that the generated image and the target image have consistent features as much as possible. We conducted subjective and objective evaluations on the mapping results of our method and eight mainstream methods. The evaluation results show that our method is superior in terms of preserving image details and enhancing image contrast. Further comparison with a parameter-free tone mapping operator using generative adversarial network (TMO-Net) indicates that our method avoids problems such as target edge blur and artifacts in the mapped images, resulting in higher visual quality of the mapped images.
Semi-supervised ROC analysis for reliable and streamlined evaluation of phenotyping algorithms

Abstract Objective High-throughput phenotyping will accelerate the use of electronic health records (EHRs) for translational research. A critical roadblock is the extensive medical supervision required for phenotyping algorithm (PA) estimation and evaluation. To address this challenge, numerous weakly-supervised learning methods have been proposed. However, there is a paucity of methods for reliably evaluating the predictive performance of PAs when a very small proportion of the data is labeled. To fill this gap, we introduce a semi-supervised approach (ssROC) for estimation of the receiver operating characteristic (ROC) parameters of PAs (eg, sensitivity, specificity). Materials and Methods ssROC uses a small labeled dataset to nonparametrically impute missing labels. The imputations are then used for ROC parameter estimation to yield more precise estimates of PA performance relative to classical supervised ROC analysis (supROC) using only labeled data. We evaluated ssROC with synthetic, semi-synthetic, and EHR data from Mass General Brigham (MGB). Results ssROC produced ROC parameter estimates with minimal bias and significantly lower variance than supROC in the simulated and semi-synthetic data. For the 5 PAs from MGB, the estimates from ssROC are 30% to 60% less variable than supROC on average. Discussion ssROC enables precise evaluation of PA performance without demanding large volumes of labeled data. ssROC is also easily implementable in open-source R software. Conclusion When used in conjunction with weakly-supervised PAs, ssROC facilitates the reliable and streamlined phenotyping necessary for EHR-based research.
A Compatibilist Approach in Ontology: Steps Towards a Formalization

Commonsense ontology often conflicts with the ontology of our best scientific and philosophical theories. However, commonsense ontology, and commonsense belief systems in general, seems to be remarkably efficient and cognitively fundamental. In cases of contrast, it is better to find a way to reconcile commonsense and “theoretical” ontologies. Given that commonsense ontologies are typically expressed within natural language, a classical procedure of reconciliation is semantical. The strategy is that of individuating the “ontologically problematic” expressions of natural language and paraphrasing the sentences in which they appear in a (formal) language whose commitments are compatible with those of our best theories. We believe that this strategy of reconciliation, though quite standard, especially in the philosophical literature, is problematic: for a start, it forces us to conclude that the “real content” of our commonsense expressions and beliefs is different from what it appears. Commonsense ontology becomes just an illusion. We will thus propose an alternative approach: according to our view, a commonsense ontology is reconciled with a theoretical ontology in case it is shown that the explanation of why we believe in the existence of a problematic entity is compatible with our best theories. We will call this kind of reconciliation “epistemic”. The advantage of an epistemic reconciliation is that commonsense ontology is treated in its own right and could be taken prima facie. Another advantage of the view is that epistemic reconciliation can be analysed through the notion of explaining away: a commonsense ontology is epistemically reconciled with a theoretical ontology if and only if the problematic entities of the commonsense ontology are explained away by “respectable” entities of the theoretical ontology. In the final part of the paper, we sketch a formal analysis of explaining away.
The impact of laboratory automation on efficiency and accuracy in healthcare settings

Automated sample processing systems, such as handlers, have played a role in expediting specimen handling, especially during emergencies. Additionally, automated analyzers have contributed to increased testing efficiency by enabling high throughput screening and quicker access to information. This article explores how the use of automated technology in laboratories has greatly improved efficiency and accuracy in healthcare settings. By examining the integration of automated systems for processing samples and conducting tests this review highlights the impact automation has had on outcomes. One notable benefit is reduced turnaround times, streamlined workflows, and enhanced precision in diagnostic testing. The incorporation of laboratory information management systems (LIMS) has further improved efficiency through data integration and real-time monitoring. Accuracy is an aspect of processes, and automated systems meticulously adhere to predefined protocols, resulting in reduced error rates and consistently reliable results. The introduction of intelligence (AI) has enhanced accuracy, particularly in image analysis within the pathology and radiology fields. Effective clinical management of laboratory automation entails technology selection planning for implementation and ongoing monitoring. Interoperability between systems, continuous education on advancements, and efficient workforce management are all crucial components for successful implementation. Despite challenges faced along the way, adopting laboratory automation is essential for optimizing laboratories' workflows while delivering timely information. The review consistently affirms laboratory automation's valid influence in improving efficiency and accuracy within healthcare environments.
Efficiency analysis of path-finding algorithms in a 2D grid environment

&lt;p&gt;This paper offers a focused overview of pathfinding algorithms, particularly emphasizing Greedy Best First Search (G-BFS) and Rapidly-Exploring Random Trees (RRT). Their performance is evaluated within a 2D grid setting tailored for Unmanned Aerial Vehicles (UAVs). Divided into two main sections, the study first expounds on the theoretical underpinnings of these algorithms, followed by empirical validation. A series of systematic experiments, involving varied 2D grid dimensions and traversal patterns, facilitates a comparative analysis between G-BFS and RRT. Importantly, the real-world implementation of these algorithms in UAV navigation underscores their practicality, illuminating their respective execution times and resource utilization. While G-BFS thrives in straightforward scenarios, RRT, especially RRT*, displays superior capability in navigating more intricate and expansive terrains, albeit with marginally extended execution durations attributed to its explorative nature.&lt;/p&gt;
FACTORS OF TRANSFORMATION OF HIGHER EDUCATION SERVICES CONSUMPTION TYPES IN THE TODAY'S RUSSIA

The article briefly describes a set of socio-technological, socio-managerial, socio-economic, socio-psychological factors that have triggered the change of the motives of the modern Russians' consumption behavior on the market of educational services in domestic higher education, such as: integration of advanced information and communication technologies into educational process, the change of conceptual approaches to the organization of the higher education system in favor of practice-oriented learning, the “obsolescence” of the content of educational programs of higher education, the decline of the quality of services offered on the Russian market of education for adults, the mismatch of consumer motives in the minds of the family acting as a collective participant on the market of educational services. The authors also set out their views on the vectors of further development of the market of services in the field of Russian higher education (the displacement of mentoring elements from the educational process in higher education; expanding the range of distance learning services; gamification of marketing strategies for promoting educational services in the youth audience; further integration of digital technologies into the organized educational process).
Cuidados ao paciente com úlceras crónicas de pernas: Construção e avaliação de software para monitorização

Background: The systematization of data records about the status and evolution of chronic leg ulcers improves their treatment. Objective: To describe the development of a software for monitoring care provided to patients with chronic leg ulcers. Methodology: This methodological study was based on Participatory Interaction Design process and developed in four interactive stages - 1) Requirements analysis; 2) (Re)design; 3) Prototype construction, and 4) Quality assessment. Results: The software includes a description of constitutional and behavioral factors, the disease, evaluation methods, data from the consultation and monitoring of recurrences, and alerts indicating the factors involved in the healing process. Moreover, all experts agreed on the criteria of usability, functionality, appearance, organization, sequence, and understandability. Conclusion: The developed software is adequate in terms of quality and may be useful for stoma nurses and generalist nurses.
A Comprehensive Survey on Security Aspects of Using Blockchain Technology for Digital Degree Verification Solutions

Education plays a critical role in a person's life, and obtaining a degree certificate along with knowledge can promise a successful career. However, when students apply for jobs, organizations manually verify their certificates, which is a tedious and time-consuming process. Unfortunately, there are instances when students present counterfeit certificates, making it challenging for organizations to identify them. The verification process for academic credentials can be complex and time-consuming. Moreover, managing digital documents can also pose its own set of challenges. Digital documents can be vulnerable to hacking and data breaches, and ensuring that the information in these documents is accurate and up-to-date can be challenging. To address these challenges, universities and other educational institutions are increasingly turning to various technologies for verifying academic credentials. Blockchain technology is not governed by a central authority due to its decentralized nature, which streamlines the verification process and improves the security and accuracy of academic credentials [1]. Additionally, this study focuses on security aspects with current blockchain-based systems for digital degree verification.
METHODOLOGICAL APPROACHES TO PREPARING FUTURE TEACHERS OF THE NATURAL SCIENCES FOR PROFESSIONAL ACTIVITIES

The article discusses the methodological approaches to the professional training of future teachers in the field of natural education, including psychological and pedagogical, competency-oriented, activity-based, integrative, praxeological, and reflexive approaches. The combination of these methodological approaches is justified for the successful preparation of future teachers in the field of natural education. The psychological and pedagogical approach helps in understanding and taking into account the individual characteristics of participants in the educational process. The competency-oriented approach focuses on developing the necessary skills and knowledge of future professionals through the use of tasks that encourage creative thinking. The activity-based approach emphasizes practical application of acquired knowledge in real conditions. The integrative approach promotes active student engagement in the educational process through discussions, innovative technologies, cooperation, and independent work, among other things. It has been found that future teachers who are prepared under an integrative approach enhance their knowledge and pedagogical practices in line with contemporary trends in natural education. Reflection helps education seekers analyze their own activities, identify their strengths and weaknesses, leading to a better understanding of themselves as future teachers. When applying the praxeological approach, students actively participate in learning through practical tasks, laboratory work, field research, projects, and more. This helps them gain practical experience and apply theoretical knowledge in practice as they learn to analyze complex situations and seek optimal solutions. Examples of implementing these methodological approaches in the educational process for the preparation of future teachers in the field of natural education are provided, specifically under the educational programs “Secondary Education (Biology and Human Health)” and “Secondary Education (Natural Sciences)”. It is demonstrated that these methodological approaches help students become competent teachers capable of effectively teaching and enhancing the learning process in the field of natural education in general secondary education institutions, as well as fostering students’ interest in this area of knowledge and more. Success in the training of teachers in natural sciences is determined by the ability of participants in the educational process not only to transmit knowledge but also to inspire and cultivate an interest in science among the younger generation.
Blockchain-based Semantic Information Sharing and Pricing for Web 3.0

Web 3.0 is a promising next-generation Internet paradigm that is driven by multiple technologies, e.g., semantic communications, besides blockchain to enable transparent and traceable data sharing. Specifically, semantic communication can provide Web 3.0 services with a promising and lightweight paradigm for semantic information transmission. However, a unified integration framework has not been studied. Moreover, how to achieve efficient semantic sharing and pricing challenges the traditional centralized communication paradigm. Therefore, this paper integrates blockchain and semantic communication to share valuable semantic information for Web 3.0 services. The Proof of Semantic mechanism is designed to reduce the garbage-in garbage-out challenge of Web 3.0 services. Moreover, we utilize the state channel and the information bottleneck to construct a task-relevant semantic sharing mechanism to exchange information efficiently in Web 3.0 services. To maximize semantic information's utility, we propose a semantic pricing mechanism based multi-leader multi-follower Stackelberg game to circulate information in Web 3.0 services. Simulation results indicate that the proposed mechanisms are both verifiable and efficient, achieving reduced communication overheads while maintaining test accuracy comparable to benchmark methods.
88 REASmash: A serious game in immersive virtual reality for the evaluation of spatial and non-spatial attention impairments in post-stroke individuals.

Objective: Stroke results in various cognitive and motor impairments. The most frequent cognitive problem is spatial and non-spatial attention, typically caused by unilateral brain lesion. Attention is typically assessed with several different paper-and-pencil tests, which have long been criticized for their lack of theoretical basis, their limited ecological validity to deficits experienced in daily life, and their lack of measurement sensitivity (Appelros et al., 2004; Azouvi, 2017). Here, our global aim was to develop an innovative integrative serious game in an immersive environment. The REASmash, combines the evaluation of spatial attention, non-spatial attention, and motor performance. We present the spatial and non-spatial cognitive attention evaluation results. Participants and Methods: Eighteen first stroke individuals and 40 age-match healthy controls were assessed on the REASmash. They were instructed to find a target mole presented amongst distractor moles. The stimulus array consisted of a grid of 6 columns and 4 rows of molehills, from which the target and 11, 17 and 23 distractors moles could randomly appear, in two search conditions (single feature condition and saliency condition). Responses were made with the ipsilesional hand for individuals with stroke and with the dominant hand for the healthy controls. Participants were evaluated also with two standardized clinical tests of attention; the hearts cancellation task of the Oxford Cognitive Screen, and the visual scanning subtest of the Test for Attentional Performance. Results: Validation results showed significant and strong correlations between the REASmash and the two reference tests, with the REASmash showing high sensitivity and specificity (i.e., the correct identification of the post-stroke vs. control individuals). The REASmash also showed significant and strong test/re-test reliability. We additionally evaluated user experience using the UEQ, and the results showed excellent attractiveness and novelty, and good stimulation and efficiency. Conclusions: In conclusion, the REASmash is a novel immersive virtual environment serious game that is valid, sensitive, and usable. It provides a new diagnosis measure spatial and non-spatial attention impairment.
A Study on PV Installation Forecasting Model for Strategic Distribution Planning

The complexity and uncertainty of the distribution system are increasing due to the growth of distributed power generations. Despite the decrease in the load growth rate, the investment cost of distribution lines is expected to continue to increase due to the expansion of the supply of renewable energies. Therefore, a mid- to long-term strategic distribution planning that considers loads and distributed power generations prospects is needed. For this, it is necessary to forecast the location of the predicted distributed power generation capacity. This is because an efficient and economical distribution planning for the expansion of distribution lines is established in consideration of the predicted location. In this paper, the model for forecasting the installation location of PV(Photovoltatic power generation), which is the majority of the distributed power generations connected to the distribution system, was proposed. At the same time, in this paper, the annual PV installation forecasting of the target area was performed through the proposed model, and the results were analyzed to examine its effectiveness.