Reviewerly/exHarmony huggingface dataset:
Comparison of Sunshine Duration Measurements between the Campbell–Stokes Sunshine Recorder and Three Automatic Sensors at Three Locations in Cyprus

This paper compares the daily sunshine duration (SD) measured by the Campbell–Stokes sunshine recorder (CS) with three automatic instruments (Kipp &amp; Zonen pyranometers (CM6 B), pyrheliometer (CHP1) and CSD3 sunshine recorders). The comparisons are essential, since recently, automatic sunshine duration sensors have been replacing the manual measurements, affecting, therefore, the continuity of the CS time series. The comparisons were applied to the daily SD data of three synoptic stations (Larnaca A/P, Pafos A/P and Athalassa) where parallel measurements of manual and automatic instruments are carried out during the periods 2009–2012 and 2012–2015. The CSD3 measurements were obtained from two automatic weather stations (AWSs) which are close to the synoptic station of Larnaca A/P. The comparisons referred to the daily and monthly sums of sunshine duration, as well as to their statistical distributions and their relationships. The daily differences in the pairs of sunshine recorders installed at the same location are mostly within the range of −1.0 to +1.0 h. At Larnaca, in the period 2009–2012, the pyranometric method’s (Gl) values were higher than the CS in the summer months. Pafos and Athalassa showed higher values of CS compared to Gl and the pyrheliometric method (Pr), respectively, almost throughout the year. Generally, the annual average daily differences between the different pairs range between 0.3 and 0.5 h. Regression equations were determined allowing the estimation of daily SD under different sky conditions. The coefficient of determination (R2) reached the values of 0.91 and 0.96 under ‘all sky’ conditions, and between 0.80 and 0.91 under ‘clear’ sky conditions at the three locations.
Enhancing Algorithm Selection through Comprehensive Performance Evaluation: Statistical Analysis of Stochastic Algorithms

Analyzing stochastic algorithms for comprehensive performance and comparison across diverse contexts is essential. By evaluating and adjusting algorithm effectiveness across a wide spectrum of test functions, including both classical benchmarks and CEC-C06 2019 conference functions, distinct patterns of performance emerge. In specific situations, underscoring the importance of choosing algorithms contextually. Additionally, researchers have encountered a critical issue by employing a statistical model randomly to determine significance values without conducting other studies to select a specific model for evaluating performance outcomes. To address this concern, this study employs rigorous statistical testing to underscore substantial performance variations between pairs of algorithms, thereby emphasizing the pivotal role of statistical significance in comparative analysis. It also yields valuable insights into the suitability of algorithms for various optimization challenges, providing professionals with information to make informed decisions. This is achieved by pinpointing algorithm pairs with favorable statistical distributions, facilitating practical algorithm selection. The study encompasses multiple nonparametric statistical hypothesis models, such as the Wilcoxon rank-sum test, single-factor analysis, and two-factor ANOVA tests. This thorough evaluation enhances our grasp of algorithm performance across various evaluation criteria. Notably, the research addresses discrepancies in previous statistical test findings in algorithm comparisons, enhancing result reliability in the later research. The results proved that there are differences in significance results, as seen in examples like Leo versus the FDO, the DA versus the WOA, and so on. It highlights the need to tailor test models to specific scenarios, as p-value outcomes differ among various tests within the same algorithm pair.
When AI Fails to See: The Challenge of Adversarial Patches

Object detection, a key application of machine learning in image processing, has achieved significant success thanks to advances in deep learning (Girshick et al. 2014). In this paper, we focus on analysing the vulnerability of one of the leading object detection models, YOLOv5x (Redmon et al. 2016), to adversarial attacks using specially designed interference known as “adversarial patches” (Brown et al. 2017). These disturbances, while often visible, have the ability to confuse the model, which can have serious consequences in real world applications. We present a methodology for generating these interferences using various techniques and algorithms, and we analyse their effectiveness in various conditions. In addition, we discuss potential defences against these types of attacks and emphasise the importance of security research in the context of the growing popularity of ML technology (Papernot et al. 2016). Our results indicate the need for further research in this area, bearing in mind the evolution of adversarial attacks and their impact on the future of ML technology.
Frankenstein: fast and lightweight call graph generation for software builds

Abstract Call Graphs are a rich data source and form the foundation for advanced static analyses that can, for example, detect security vulnerabilities or dead code. This information is invaluable when it is immediately available, such as in the output of a build system. Call Graph generation is a whole-program analysis: not just the application, but also all its dependencies are processed together. Recent work has shown that even advanced static analyses can use summarization techniques to substantially improve runtime; however, existing analyses focus on soundness, and as such remain very expensive. When executed in the build system, which typically has limited resources, even powerful servers suffer from slow build times, rendering these analyses impractical in today’s fast-paced development. In this paper, we aim to strike a balance between improving static analyses while remaining practical for use cases that require quick results in low-resource environments. We propose a summarization-based implementation of a Class-Hierarchy Analysis algorithm for call graph generation of Java programs. Our approach leverages the fact that dependency sets often do not change between builds: we can generate call graphs for these dependencies, cache their generation for subsequent builds, and using a novel stitching algorithm, Frankenstein, merge all partial results into a complete call graph for the whole program. Our evaluation results show that this lightweight approach can substantially outperform existing frameworks. In terms of speed improvements, Frankenstein surpasses the baselines by up to 38%, requiring an average of just 388 Megabytes of memory. This makes the proposed approach practical for build systems with limited memory resources. Despite these optimizations, our generated call graphs maintain a near-identical set of edges when compared to the baselines, achieving an F $$_{1}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msub> </mml:math> score of up to 0.98. This summarization-based approach for call graph generation paves the way for using extended static analyses in build processes.
Neuroadaptive Incentivization in Healthcare using Blockchain and IoT

Abstract Financially incentivizing health-related behaviors can improve health record outcomes and reduce healthcare costs. Blockchain and IoT technologies can be used to develop safe and transparent incentive schemes in healthcare. IoT devices, such as body sensor networks and wearable sensors, etc. connect the physical and digital world making it easier to collect useful health-related data for further analysis. There are, however, many security and privacy issues with the use of IoT. Some of these IoT security issues can be alleviated using Blockchain technology. Incorporating neuroadaptive technology can result in more personalized and effective therapies using machine learning algorithms and real-time feedback. The research investigates the possibilities of neuroadaptive incentivization in healthcare using Blockchain and IoT on patient health records. The core idea is to incentivize patients to keep their health parameters within standard range thereby reducing the load on healthcare system. In summary, we have presented a proof of concept for neuroadaptive incentivization in healthcare using Blockchain and IoT and discuss various applications and implementation challenges.
A Knowledge Graph Embedding Model Based on Cyclic Consistency—Cyclic_CKGE

Most of the existing medical knowledge maps are incomplete and need to be completed/predicted to obtain a complete knowledge map. To solve this problem, we propose a knowledge graph embedding model (Cyclic_CKGE) based on cyclic consistency. The model first uses the “graph” constructed with the head entity and relationship to predict the tail entity, and then uses the “inverse graph” constructed with the tail entity and relationship to predict the head entity. Finally, the semantic space distance between the head entity and the original head entity should be very close, which solves the reversibility problem of the network. The Cyclic_CKGE model with a parameter of 0.46 M has the best results on FB15k-237, reaching 0.425 Hits@10. Compared with the best model R-GCN, its parameter exceeds 8 M and reaches 0.417 Hits@10. Overall, Cyclic_CKGE’s parametric efficiency is more than 17 times that of R-GCNs and more than 8 times that of DistMult. In order to better show the practical application of the model, we construct a visual medical information platform based on a medical knowledge map. The platform has three kinds of disease information retrieval methods: conditional query, path query and multi-symptom disease inference. This provides a theoretical method and a practical example for realizing knowledge graph visualization.
Hierarchical Keyword Generation Method for Low-Resource Social Media Text

The exponential growth of social media text information presents a challenging issue in terms of retrieving valuable information efficiently. Utilizing deep learning models, we can automatically generate keywords that express core content and topics of social media text, thereby facilitating the retrieval of critical information. However, the performance of deep learning models is limited by the labeled text data in the social media domain. To address this problem, this paper presents a hierarchical keyword generation method for low-resource social media text. Specifically, the text segment is introduced as a hierarchical unit of social media text to construct a hierarchical model structure and design a text segment recovery task for self-supervised training of the model, which not only improves the ability of the model to extract features from social media text, but also reduces the dependence of the keyword generation model on the labeled data in the social media domain. Experimental results from publicly available social media datasets demonstrate that the proposed method can effectively improve the keyword generation performance even given limited social media labeled data. Further discussions demonstrate that the self-supervised training stage based on the text segment recovery task indeed benefits the model in adapting to the social media text domain.
Keyword-labeled self-admitted technical debt and static code analysis have significant relationship but limited overlap

Abstract Technical debt presents sub-optimal choices made in development, which are beneficial in the short term but not in the long run. Consciously admitted debt, which is marked with a keyword, e.g., TODO, is called keyword-labeled self-admitted technical debt (KL-SATD). KL-SATD can lead to adverse effects in software development, e.g., to a rise in complexity within the developed software. We investigated the relationship between KL-SATD from source code comments and reports from the highly popular industrial program analysis tool SonarQube. The goal was to find which SonarQube metrics and issues are related to KL-SATD introduction and removal and how many KL-SATD in the context of an issue addresses that issue. We performed a study with 33 software repositories. We analyzed the changes in SonarQube reports (sqale index, reliability and security remediation metrics, and SonarQube issues) and the relationship to KL-SATD addition and removal with mixed model analysis. We manually annotated a sample to investigate how many KL-SATD comments are in the context of SonarQube issues and how many address them directly. KL-SATD is associated with a reduction in code maintainability measured with SonarQube’s sqale index. KL-SATD removal is associated with an increase in code maintainability (sqale index) and reliability measured with SonarQube’s reliability remediation effort. The introduction and removal of KL-SATD have a predominantly relationship with code smells, and not with vulnerabilities and bugs. Manual annotation revealed that 36% of KL-SATD comments are in the context of a SonarQube issue, but only 15% of the comment address an issue. This means that despite of statistical relationship between KL-SATD comments and SonarQube reports there is a large set of KL-SATD comments that are in areas that Sonarqube reports as clean or free of maintainability issues. KL-SATD introduction and removal are connected mainly to code smells, connecting them to maintainability rather than reliability or security. This is reinforced by the relationship with the sqale index, as well as the dominance of code smells in SonarQube issues. Many KL-SATD issues have characteristics going beyond static analysis tools and require future studies extending the capabilities of the current tools. As KL-SATD comments and SonarQube reports appear to have limited overlap, it suggests that they are complementary and both are needed for getting a comprehensive view coverage of code maintainability. The study also presents rules violations developers should be aware of regarding KL-SATD introduction and removal.
Design and research activity of students as a pedagogical problem

This article presents the origins of the design and research activities of students. A system of means that form the design and research activities of students has been developed. Four types of design and research activities of students (imitative, reproductive, partially exploratory and creative) and the mechanisms of their formation are described. The main ways of activating the project activity of students are revealed: the formation of learning motives, the use of problematic elements, the system of various types of project work, the connection of the studied material with the life and personal experience of the student. Work built on active cognitive activity brings students feelings of deep satisfaction, develops their imagination , develops a creative initiative, forms a worldview and moral character. We have made an attempt to analyze the theoretical foundations, methods and techniques of activating the project activity of students in the lessons of the course of mathematical analysis.
Formation of research culture of students in the teaching of physics in higher education

In the article the possibilities of development of research cul-ture of students of non-skew professional pedagogical education. The research culture of students is a brief description of his level of qualification for his selfsocialization, his abilities, skills and knowledge. He also demonstrates that education, knowledge, and skills in research cul-ture the basis for the training of future teachers and are important for the development of the individual. Scientific - research culture of students of non-spry professional pedagogical education.
Estimating a Potential Without the Agony of the Partition Function

Estimating a Gibbs density function given a sample is an important problem in computational statistics and statistical learning. Although the well established maximum likelihood method is commonly used, it requires the computation of the partition function (i.e., the normalization of the density). This function can be easily calculated for simple low-dimensional problems but its computation is difficult or even intractable for general densities and high-dimensional problems. In this paper we propose an alternative approach based on maximum a posteriori (MAP) estimators, which we name maximum recovery MAP, to derive estimators that do not require the computation of the partition function, and reformulate the problem as an optimization problem. We further propose a least-action type potential that allows us to quickly solve the optimization problem as a feed-forward hyperbolic neural network. We demonstrate the effectiveness of our methods on some standard data sets.
An improved hierarchical clustering method based on the <scp>k‐NN</scp> and density peak clustering

Abstract Clustering is one of the most prevalent and important data mining algorithms ever developed. Currently, most clustering methods are divided into distance‐based and density‐based. In 2014, the fast search and find of density peaks clustering method was proposed, which is simple and effective and has been extensively applied in several research domains. However, the original version requires manually assigning a cut‐off distance and selecting core points. Therefore, this article improves the density peak clustering method from two aspects. First, the Gaussian kernel is substituted with a k‐nearest neighbors method to calculate local density. This is important as compared with selecting a cut‐off distance, calculating the k ‐value is easier. Second, the core points are automatically selected, unlike the original method that manually selects the core points regarding local density and distance distribution. Given that users' selection influences the clustering result, the proposed automatic core point selection strategy overcomes the human interference problem. Additionally, in the clustering process, the proposed method reduces the influence of manually assigned parameters.
A General Model for Side Information in Neural Networks

We investigate the utility of side information in the context of machine learning and, in particular, in supervised neural networks. Side information can be viewed as expert knowledge, additional to the input, that may come from a knowledge base. Unlike other approaches, our formalism can be used by a machine learning algorithm not only during training but also during testing. Moreover, the proposed approach is flexible as it caters for different formats of side information, and we do not constrain the side information to be fed into the input layer of the network. A formalism is presented based on the difference between the neural network loss without and with side information, stating that it is useful when adding side information reduces the loss during the test phase. As a proof of concept we provide experimental results for two datasets, the MNIST dataset of handwritten digits and the House Price prediction dataset. For the experiments we used feedforward neural networks containing two hidden layers, as well as a softmax output layer. For both datasets, side information is shown to be useful in that it improves the classification accuracy significantly.
Stylus and Gesture Asymmetric Interaction for Fast and Precise Sketching in Virtual Reality

This research investigates fast and precise Virtual Reality (VR) sketching methods with different tool-based asymmetric interfaces. In traditional real-world drawing, artists commonly employ an asymmetric interaction system where each hand holds different tools, facilitating diverse and nuanced artistic expressions. However, in virtual reality (VR), users are typically limited to using identical tools in both hands for drawing. To bridge this gap, we aim to introduce specifically designed tools in VR that replicate the varied tool configurations found in the real world. Hence, we developed a VR sketching system supporting three hybrid input techniques using a standard VR controller, a VR stylus, or a data glove. We conducted a formal user study consisting of an internal comparative experiment with four conditions and three tasks to compare three asymmetric input methods with each other and with a traditional symmetric controller-based solution based on questionnaires and performance evaluations. The results showed that in contrast to symmetric dual VR controller interfaces, the asymmetric input with gestures significantly reduced task completion times while maintaining good usability and input accuracy with a low task workload. This shows the value of asymmetric input methods for VR sketching. We also found that the overall user experience could be further improved by optimizing the tracking stability of the data glove and the VR stylus.
A Comprehensive Survey on Methods for Image Integrity

The outbreak of digital devices on the Internet, the exponential diffusion of data (images, video, audio, and text), along with their manipulation/generation also by Artificial Intelligence (AI) models, e.g., Generative Adversarial Networks (GANs), have created a great deal of concern in the field of forensics. A malicious use can affect relevant application domains, which often include counterfeiting biomedical images, and deceiving biometric authentication systems, as well as their use in scientific publications, in the political world, and even in school activities. It has been demonstrated that manipulated pictures most likely represent indications of malicious behavior, such as photos of minors to promote child prostitution or false political statements. Following this widespread behavior, various forensic techniques have been proposed in the scientific literature over time both to defeat these spoofing attacks as well as to guarantee the integrity of the information. Focusing on Image Forensics, which is currently a very hot topic area in Multimedia Forensics, this paper will discuss the whole scenario in which a target image could be modified. The aim of this comprehensive survey will be 1) to provide an overview of the types of attacks and contrasting techniques and 2) to evaluate to what extent the former can deceive prevention methods and the latter can identify counterfeit images. The results of this study highlight how forgery detection techniques, sometimes limited to a single type of real scenario, are not able to provide exhaustive countermeasures and could/should therefore be combined. Currently, the use of neural networks, such as CNNs, is already heading, synergistically, in this direction.